Efficient Matching of Service Providers and Service Requests Across a Fleet of Autonomous Vehicles

ABSTRACT

In one or more embodiments, one or more systems, methods, and/or processes may receive respective service center candidacy information from service center computers; determine that an autonomous vehicle requires service work; determine a pool of service centers that are capable of providing the service work for the autonomous vehicle; select, based at least on a reduction in a utilization of at least one resource, a service center from the pool of service centers; and provide, to the autonomous vehicle computing device associated with the autonomous vehicle, location information of the selected service center.

BACKGROUND

An autonomous vehicle may be a transportation vehicle capable of drivingand navigating based on data gathered by its sensors and the processingof such data by an onboard computing system. Unlike conventional,human-driven vehicles, an autonomous vehicle may need little or no inputfrom a human driver to safely drive and navigate. However, likeconventional vehicles, autonomous vehicles also need periodicmaintenance and servicing, such as being refueled, cleaned, calibrated,or repaired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C illustrate examples of a transportation managementenvironment, according to one or more embodiments.

FIG. 2A illustrates examples of various systems, according to one ormore embodiments.

FIGS. 2B and 2C illustrate examples of user interfaces, according to oneor more embodiments.

FIG. 3 illustrates an example method for determining a score of aservice center, according to one or more embodiments.

FIGS. 4A and 4B illustrate an example method for operating atransportation management system, according to one or more embodiments.

FIG. 5 illustrates an example method for matching autonomous vehicleswith service centers, according to one or more embodiments.

FIG. 6 illustrates an example method for matching autonomous vehicleswith service entities, according to one or more embodiments.

FIG. 7 illustrates an example method for operating a transportationmanagement system, according to one or more embodiments.

FIG. 8 illustrates another example block diagram of a transportationmanagement environment for matching ride requestors with autonomousvehicles, according to one or more embodiments.

FIG. 9 illustrates an example of a computing system, according to one ormore embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described. In addition, the embodiments disclosedherein are only examples, and the scope of this disclosure is notlimited to them. Particular embodiments may include all, some, or noneof the components, elements, features, functions, operations, or stepsof the embodiments disclosed above. Embodiments according to theinvention are in particular disclosed in the attached claims directed toa method, a storage medium, a system and a computer program product,wherein any feature mentioned in one claim category, e.g., method, canbe claimed in another claim category, e.g., system, as well. Thedependencies or references back in the attached claims are chosen forformal reasons only. However any subject matter resulting from adeliberate reference back to any previous claims (in particular multipledependencies) can be claimed as well, so that any combination of claimsand the features thereof are disclosed and can be claimed regardless ofthe dependencies chosen in the attached claims. The subject-matter whichcan be claimed comprises not only the combinations of features as setout in the attached claims but also any other combination of features inthe claims, wherein each feature mentioned in the claims can be combinedwith any other feature or combination of other features in the claims.Furthermore, any of the embodiments and features described or depictedherein can be claimed in a separate claim and/or in any combination withany embodiment or feature described or depicted herein or with any ofthe features of the attached claims.

Operating a successful transportation service using autonomous vehiclesinvolves more than matching and dispatching the autonomous vehicles tofulfill ride requests from ride requestors. Beyond the hardware andsoftware for matching rides, new technology is needed for autonomousvehicle management. For example, in a transportation service, such as aridesharing transportation service in which ride providers (i.e., humandrivers) drive their own vehicles, the ride providers are responsiblefor maintaining their own vehicles. On the other hand, a transportationservice that also uses autonomous vehicles to fulfill ride requests isresponsible for ensuring that the autonomous vehicles are kept in goodworking order and that the autonomous vehicles meet applicable safetyand regulatory requirements. Like conventional human-driven vehicles,autonomous vehicles also need maintenance from time-to-time. In oneexample, the need for maintenance may be associated with one or moreissues and/or problems. For instance, the one or more issues and/orproblems may include one or more of a failed sensor, a malfunctioningsensor, a burned out headlight, a burned out taillight, a burned outturn signal light, a burned out cabin light, low tire pressure, a clogor an obstruction in a cabin air distribution system, a blown fuse, abroken window or windshield, a broken or failing air conditioningsystem, an oil leak, a fuel leak, a coolant leak, low oil pressure, anda low level of coolant, among others. In a second example, themaintenance may be preventative maintenance. For instance, thepreventative maintenance may include one or more of tire replacement,adding air to one or more tires, air filter replacement, adding coolant,adding oil, an oil change, and one or more firmware updates and/orsoftware updates, among others. In another example, the maintenance maybe associated with cleaning the autonomous vehicles and/or inspectingthe autonomous vehicles. In one instance, cleaning an autonomous vehiclemay include one or more of washing the outside of the autonomousvehicle, vacuuming the inside of the autonomous vehicle, and washing theinsides of the windows of the autonomous vehicle, among others. Inanother instance, inspecting an autonomous vehicle may include one ormore of viewing the exterior of the autonomous vehicle for damage (e.g.,dents, dings, hail damage, rock damage, scratches, scrapes, etc.),viewing the interior of the autonomous vehicle for damage (e.g.,scratches, scrapes, tearing of a seat, tearing of a carpet, etc.),viewing the tires on the autonomous vehicle for wear, looking for trashleft in the autonomous vehicle, and looking for liquid spills (e.g.,soda spills, coffee spills, etc.), among others.

Despite the certainty or near certainty that autonomous vehicles wouldneed to be serviced periodically, autonomous vehicles are not configuredto handle their own service/maintenance scheduling. Furthermore, when anautonomous vehicle is part of a fleet of autonomous vehicles operated bya transportation service, the time at which the vehicle is temporarilytaken offline to be serviced (e.g., out of the available pool ofvehicles that may be dispatched to fulfill ride requests) and theduration of the offline period would affect the overall operation of thefleet, especially when ride demand is high. An autonomous vehicle,however, is not configured to take such fleet-level impact intoconsideration.

In particular embodiments, a transportation management system thatmanages a fleet of autonomous vehicles for servicing ride requests mayalso manage the vehicles' maintenance. For example, the transportationmanagement system may schedule autonomous vehicles for service duringperiods of predicted low demand and/or based at least on the urgency ofaddressing the needed services. In particular embodiments, a subset ofneeded services may be performed during a short window of predicted lowdemand or during a period of high or peak demand if, for example, avehicle does not meet minimum maintenance requirements for beingincluded in a pool of autonomous vehicles available to fulfill riderequests. When scheduling autonomous vehicles for service, atransportation management system may also take into consideration thecapabilities, available capacity, speed of operations, and/or locationsof various service centers.

In particular embodiments, a transportation service provider associatedwith a transportation management system may operate service centers thatare accessible from a region. Operating such facilities, however, may beexpensive and cost-prohibitive, as it requires real estate, equipment,and service technicians. This may especially true for highly populatedareas, such as cities, where real estate is scarce. While it may bepossible to have facilities outside of but accessible from highlypopulated areas, servicing autonomous vehicles at such locations maymean increased offline durations, for vehicles, since the vehicles wouldhave to drive outside to the service centers and then drive back to thearea where demand is high. This in turn would negatively impact thevehicle's utilization and the service capacity of the fleet overall.

To address these problems and others, particular embodiments describedherein relate to systems and methods that match autonomous vehiclesneeding service with existing third-party service providers. Sincethird-party service providers (e.g., automobile service and repairfacilities) already exist in most regions to service conventionalhuman-driven vehicles, the transportation management system may leveragetheir expertise, equipment, personnel, and locations. Not only woulddoing so decrease a transportation management system's barrier-to-entryinto a particular region, it would also expand the network of serviceproviders available to the autonomous vehicles so that they may beserviced near their current location. Without having to spend timecommuting to and from distant service providers, autonomous vehiclewould be more fully utilized (due to less time being offline) and thefleet as a whole would be better suited to satisfy ridership demands. Inaddition, the increased service capacity offered by the network ofservice providers would also help decrease wait time for service.

The embodiments described herein that enable third-party serviceproviders to be matched with autonomous vehicles also benefit thethird-party service providers. Third-party service providers, such asexisting automobile maintenance and repair facilities, may often beoperating at less than full capacity, especially during certain non-peakhours (e.g., night time, weekdays, etc.). In addition, individuals whomay be experienced with or trained to service vehicles may also haveexcess capacity and desire to put their skills to use. Embodimentsdescribed herein would enable these willing third-party serviceproviders (whether third-party individuals or maintenance and repairfacilities) to make use of their excess bandwidth, and at the same timehelp meet the service needs of the fleet of autonomous vehicles.

Since a transportation management system may be deployed in a variety ofenvironments, particular embodiments described herein further relates tosystems and methods for matching autonomous vehicles with serviceproviders in a robust manner. As previously described, service providersmay include internal service providers operated by the transportationservice, third-party repair and maintenance facilities, and third-partyindividuals. Not only may there be different types of service providers,service providers of the same type may also have differentcharacteristics, which may be related to, e.g., location, maximumcapacity, available capacity, services offered, service or operationalcosts (labor, parts, storage, real estate), inventory, nearby trafficpatterns, disruption risk, environmental factors, among others.Furthermore, a transportation service may be deployed or expand intoregions with different geographic characteristics, such as differenttraffic patterns, ride demand patterns, population density, and/ortransportation infrastructure, among others. Moreover, the fleet ofvehicles servicing a particular region may have unique characteristics,such as fleet size and vehicle characteristics, including, e.g., maximumdriving range before refueling, maximum speed, weather tolerance, dayand night driving abilities, current condition, and/or maintenanceneeds, among others. Since characteristics of the available pool ofservice providers may be diverse, the systems and methods for matchingautonomous vehicles with the service providers would need to be robustto avoid needing to have custom systems and methods tailored for eachservice region.

Particular embodiments of a matching process may take into account avariety of factors when determining how to match an autonomous vehiclewith a service provider. In one example, a matching process may takeinto account distance between a service provider and an autonomousvehicle, or an estimated amount of fuel (whether gasoline or batterycharge) needed for an autonomous vehicle to reach a service provider. Inanother example, the matching process may additionally or alternativelytake into consideration whether a service provider is a third-partyrelative to the transportation service system, and/or whether a serviceprovide has capacity or is likely to complete the needed service withina threshold timeframe. Further embodiments and examples are described infurther detail below.

Utilizing various information from service providers and/or fromhistoric data, a transportation management system may match autonomousvehicles with service providers, which may provide various benefitsand/or advantages. In one example, matching autonomous vehicles withservice providers may maximize availability of autonomous vehicles toride requesters. For instance, people who need transportation or cannototherwise drive a vehicle may benefit from having autonomous vehiclesavailable when needed. In a second example, matching autonomous vehicleswith service providers may minimize fuel and/or energy consumption. Inone instance, an autonomous vehicle may be matched to a first servicecenter that is closer to the autonomous vehicle than a second servicecenter. In a second instance, autonomous vehicles may be operating in aremote region, and the autonomous vehicles may be matched to a serviceprovider that operates a mobile service center, which may be driven to alocation suitable for the autonomous vehicles operating in the remoteregion. In another example, basing a match, at least on historical data,to a first service provider, riders may benefit from a track record ofthe first service provider that is better than a track record of asecond service provider. In one instance, the historical data mayindicate that service work from the first service provider has fewerrecalls than service work from the second service provider. In secondinstance, the historical data may indicate that parts used by forservice work from the first service provider are more robust than partsused by for service work from the second service provider. In anotherinstance, the historical data may indicate that the first serviceprovider has a better quality control for service work than the secondservice provider.

Turning now to the Figures, FIG. 1A illustrates an example of atransportation management environment 100, according to one or moreembodiments. As used herein, a reference numeral refers to a class ortype of entity, and any letter following such reference numeral refersto a specific instance of a particular entity of that class or type.Thus, for example, a hypothetical entity referenced by ‘12A’ may referto a particular instance of a particular class/type, and the reference‘12’ may refer to a collection of instances belonging to that particularclass/type or any one instance of that class/type in general.

As shown, a transportation management system 110 may be communicativelycoupled to a network 120. In particular embodiments, transportationmanagement system 110 may include one or more computer systems and/orone or more databases, among others. For example, one or more of the oneor more computer systems and/or the one or more databases may bedistributed across and/or throughout a geographic region of any size. Inparticular embodiments, transportation management system 110 may receivea request for transportation from a computing device 130 of a riderequestor 135. In response to receiving the request, transportationmanagement system 110 may provide a message to a computing device 150 ofan autonomous vehicle 160. In one example, the message may include oneor more of a pickup location (e.g., a first address, firstlatitude/longitude coordinates, etc.), turn-by-turn directions to thepickup location, and/or other information. In another example, themessage may include one or more of a destination location (e.g., asecond address, second latitude/longitude coordinates, etc.),turn-by-turn directions to the destination location, and/or otherinformation. For instance, the destination location may be or include adrop-off location. In particular embodiments, the request fortransportation may include multiple destinations. For example, riderequestor 135 may request transportation to various locations. Forinstance, ride requestor 135 may be running errands (e.g., two or moreof a banking errand, a lunch errand, a post office errand, a petgrooming errand, a pharmacy errand, etc.). In particular embodiments,computing device 130 and computing device 150 may be coupled to network120. For example, network 120 may include one or more of a wirednetwork, an optical network, and a wireless network, among others,described further below.

In particular embodiments, transportation management environment 100 mayinclude various ride requestors 135 and various autonomous vehicles 160,and transportation management system 110 may match ride requestors 135with autonomous vehicles 160 and may maximize availability of autonomousvehicles 160. For example, transportation management environment 100 maymaximize availability of autonomous vehicles 160 by generating logisticsand/or coordinating service work of autonomous vehicles 160, describedfurther below.

As shown, fleet management environment 100 may include transportationmanagement system 110 and one or more service centers 180, 181, and/or182 at which autonomous vehicles 160 may be serviced. In particularembodiments, one or more service centers 180, 181, and/or 182 may beowned and/or operated by one or more service providers. In one example,a first service provider may own and/or operate a first stationaryservice center 180. In a second example, a second service provider mayown and/or operate a second stationary service center 180.

In particular embodiments, a service provider may be or include an ownerand/or operator of autonomous vehicles 160. For example, the ownerand/or operator of autonomous vehicles 160 provide various servicecenters 180 and/or 181 to provide service work to autonomous vehicles160. For instance, the owner and/or operator of autonomous vehicles 160provide stationary service center 180A and/or mobile service center 181Ato provide service work to autonomous vehicles 160. In particularembodiments, service provider may be or include a corporation (e.g., athird-party) that may not be directly associated with an owner and/oroperator of autonomous vehicles 160 but may provide service work toautonomous vehicles 160 via one or more service centers 180 and/or 181that are owned and/or operated by the corporation. For example, one ormore of stationary service center 180B and/or mobile service center 181Bmay be owned and/or operated by the corporation, which may provideservice work to autonomous vehicles 160. For instance, one or more ofstationary service center 180B and/or mobile service center 181B may bethird-party service centers.

In particular embodiments, a service provider may be an individual(e.g., a third-party). In one example, an individual service center 182Amay be or include a garage or a driveway of the individual, where theindividual may provide service work for one or more autonomous vehicles160. In another example, an individual service center 182B may be orinclude a vehicle of the individual, where the individual may travel tovarious locations to provide service work for one or more autonomousvehicles 160. In particular embodiments, an individual service center182 may be a third-party service center.

As illustrated, one or more service centers 180, 181, and/or 182 may becoupled to network 120. In particular embodiments, one or more servicecenters 180, 181, and/or 182 and transportation management system 110may communicate and/or exchange information via network 120. Forexample, one or more service centers 180, 181, and/or 182 mayrespectively include one or more computing devices that may communicateand/or exchange information via network 120.

In particular embodiments, one or more service centers 180, 181, and/or182 may request an autonomous vehicle 160. For example, one or moreservice centers 180, 181, and/or 182 may include a gas station, chargingstation, a parking facility, a cleaning facility, and/or a maintenancefacility, which may send a notification, via network 120, totransportation management system 110 to indicate an availability at arespective service center. For instance, one or more service centers180, 181, and/or 182 may notify transportation management system 110 ofavailable capacity to service vehicles. In particular embodiments,transportation management system 110 may then match autonomous vehicles160 for service, as such autonomous vehicles that require service and/ormaintenance. For example, transportation management system 110 mayreceive data from a vehicle sensor in an autonomous vehicle 160indicating that autonomous vehicle 160 requires service and/ormaintenance. For instance, sensors (e.g., weight sensors, moisturesensors, etc.), and/or in-vehicle cameras, and/or feedback submitted bya user (e.g., a rider, passenger, etc.) may be used to dispatch avehicle to a service center, which may be capable of performingdifferent cleaning services (e.g., wet cleans, dry cleans, etc.). Inparticular embodiments, transportation management system 110 may thenmatch autonomous vehicle 160 to a service center of one or more ofservice centers 180, 181, and/or 182, which indicated available spaceand may perform appropriate service and/or maintenance for autonomousvehicle 160.

Turning now to FIG. 1B, another example diagram of transportationmanagement environment 100 is illustrated, according to one or moreembodiments. At any given point in time, autonomous vehicles 160 mayinclude a pool of autonomous vehicles (e.g., 160A-160D) available tofulfill ride requests of ride requestors 135, and may also include oneor more autonomous vehicles (e.g., 160E-160H) that are not available tofulfill ride requests. Autonomous vehicles that are unavailable mayinclude those that are scheduled for and/or receiving service work at aservice center 180 and/or at a mobile service center 181. For example,autonomous vehicles 160 may fulfill ride requests of ride requestors135A-135C, at respective locations 125A-125C (e.g., pickup locations),by transporting ride requestors 135A-135C to one or more destinationlocations 125D-125F (e.g., drop-off locations). For instance,transportation management system 110 may determine a prediction ofdemand for autonomous vehicles 160 in a fleet of autonomous vehiclesbased at least on data representing current conditions and future eventsand may schedule an autonomous vehicle 160 for service work at one ormore service centers 180 and/or 181 based on the prediction, among otherfactors. Transportation management system 110 may send instructions 155to an autonomous vehicle 160 causing autonomous vehicle 160 to drive toa service center 180 or 181, where the scheduled service work is to beperformed.

In one example, transportation management system 110 may have determinedthat autonomous vehicle 160E should be scheduled for one or moreservices at stationary service center 180A. For instance, transportationmanagement system 110 may have matched autonomous vehicle 160E withstationary service center 180A. In response to this determination,transportation management system 110 may provide instructions 155A toautonomous vehicle 160E to drive (or return) to service center 180A forservice work. In accordance with instructions 155A, autonomous vehicle160E may be removed from the pool of autonomous vehicles available tofulfill ride requests, a condition that may also be referred to as beingtaken “offline”, and may drive to service center 180A for service work.After an autonomous vehicle (e.g., 160F) has been serviced by astationary service center 180, it may return to the pool of availableautonomous vehicles.

In another example, transportation management system 110 may havedetermined that autonomous vehicle 160G should be scheduled for one ormore services at mobile service center 181B. For instance,transportation management system 110 may have matched autonomous vehicle160G with mobile service center 181B. In response to this determination,transportation management system 110 may provide instructions 155C toautonomous vehicle 160G to drive (or return) to service center 181B forservice work. In accordance with instructions 155C, autonomous vehicle160G may be removed from the pool of autonomous vehicles available tofulfill ride requests, a condition that may also be referred to as beingtaken “offline”, and may drive to service center 181B for service work.In some cases, autonomous vehicle 160G may arrive at location 198 beforemobile service center 181B arrives at location 198. In other cases,mobile service center 181B may arrive at location 198 before autonomousvehicle 160G arrive at location 198. After an autonomous vehicle (e.g.,160H) has been serviced by a mobile service center 181, it may return tothe pool of available autonomous vehicles.

In particular embodiments, transportation management system 110 maymatch autonomous vehicle 160G with mobile service center 181B, ratherthan stationary service center 180B. For example, some autonomousvehicles may be deployed in areas that may be considered remote from oneor more stationary service centers 180, and transportation managementsystem 110 may provide instructions to some autonomous vehicles totravel to a location 196 or 188 to be serviced by a respective mobileservice center 181A or 181B.

In particular embodiments, transportation management system 110 mayprovide instructions 155D to mobile service center 181B. In one example,instructions 155D may instruct a driver of mobile service center 181B todrive mobile service center 181B to location 198. In another example,mobile service center 181B may be an autonomous vehicle, which mayutilize instructions 155D to navigate to location 198. In one instance,instructions 155D may include coordinates of location 198. In anotherinstance, instructions 155D may include turn-by-turn instructions tonavigate mobile service center 181B to location 198.

In particular embodiments, autonomous vehicles 160F and 160H may returnto a pool of autonomous vehicles available to fulfill ride requests. Forexample, transportation management system 110 may provide instructions155B to autonomous vehicle 160F to drive to a location (e.g., a locationof locations 125D-125F, among others), at which point autonomous vehicle160F may be returned to the pool of autonomous vehicles available tofulfill ride requests.

In particular embodiments, if an autonomous vehicle 160, that isproviding a transportation service to one or more passengers, detects asystem malfunction, transportation management system 110 may respond byarranging alternative transportation for the one or more passengers andguiding the impaired autonomous vehicle to a service center. Forexample, autonomous vehicle 160B may be providing a transportationservice to one or more passengers and a system malfunction may bedetected. In one instance, autonomous vehicle 160B may detect the systemmalfunction. In another instance, transportation management system 110may detect the system malfunction.

In particular embodiments, detecting a system malfunction may includereceiving data from a sensor of autonomous vehicle 160. For example,autonomous vehicle computing device 150 and/or transportation managementsystem 110 may receive that data from the sensor of autonomous vehicle160 and/or may detect the system malfunction. In one instance, if acomparison of the data with one or more metrics exceeds a threshold,autonomous vehicle computing device 150 and/or transportation managementsystem 110 may determine that there is the system malfunction. Inanother example, if a comparison of the data with one or more metricsfalls below a threshold, device 150 and/or transportation managementsystem 110 may determine that there is the system malfunction. Inparticular embodiments, detecting a system malfunction may includedetermining that data from a sensor of autonomous vehicle 160 has notbeen received within an amount of time. For example, if autonomousvehicle computing device 150 and/or transportation management system 110does not receive data from the sensor of autonomous vehicle 160 withinthe amount of time, autonomous vehicle computing device 150 and/ortransportation management system 110 may determine that there is thesystem malfunction.

In particular embodiments, if autonomous vehicle computing device 150detects a system malfunction with its autonomous vehicle 160, autonomousvehicle computing device 150 may notify transportation management system110. For example, autonomous vehicle computing device 150 may provideinformation that a malfunction has been detected and/or informationassociated with a detected malfunction to transportation managementsystem 110 via network 120. In particular embodiments, if transportationmanagement system 110 determines or receives information that autonomousvehicle 160 is experiencing a service related issue, such as a systemmalfunction, while autonomous vehicle 160 is providing a transportationservice to one or more passengers, transportation management system 110may provide commands and/or instructions to autonomous vehicle 160,which may cause autonomous vehicle 160 may navigate to a safe areaand/or location. For example, autonomous vehicle 160B may beexperiencing a service related issue, and transportation managementsystem 110 may provide commands and/or instructions to autonomousvehicle 160B, which may cause autonomous vehicle 160B may navigate to asafe area and/or location 194, as illustrated in FIG. 1C. For instance,safe area and/or location 194 may be or include a parking lot, ashoulder of a road, etc.

In particular embodiments, the owner/operator of the fleet of autonomousvehicles may utilize one or more third-party service centers from thepool of service centers. For example, utilizing a third-party servicecenter may reduce utilization of one or more resources (e.g., energyresources, time resources, cost resources, etc.) in servicing one ormore autonomous vehicles of the fleets of autonomous vehicles. Forinstance, the owner/operator of the fleet of autonomous vehicles may ownand/or operate service center 180A and may utilize a third-party servicecenter (e.g., 180B, 181B, 182A, 182D, 182B, 182C, 182E, etc.) to reduceutilization of one or more resources (e.g., energy resources, timeresources, cost resources, etc.) in servicing one or more autonomousvehicles 160A-160D, among others. As shown, for example, service center180A may be located in a remote area and/or region compared to an areaand/or region where autonomous vehicles 160A-160D are operating.

In particular embodiments, after determining or receiving informationthat autonomous vehicle 160 is experiencing a service related issue,such as a system malfunction, while autonomous vehicle 160 is providinga transportation service to one or more passengers, transportationmanagement system 110 may direct, instruct, and/or navigate anotherautonomous vehicle 160 to a location at least proximate to the locationof autonomous vehicle 160 that is experiencing the service relatedissue, so that the one or more passengers may utilize the otherautonomous vehicle 160 to arrive at a destination associated with theone or more passengers (e.g., a drop-off location). For example,transportation management system 110 may direct, instruct, and/ornavigate autonomous vehicle 160C to a location at least proximate tosafe area and/or location 194, so that the one or more passengers mayutilize autonomous vehicle 160C to arrive at a destination associatedwith the one or more passengers (e.g., a drop-off location, such aslocation 125D, 125E, 125F, etc.).

In particular embodiments, transportation management system 110 maydetermine and/or compute a route to a location when autonomous vehicle160 suffers a malfunction and/or failure. In one example, autonomousvehicle 160 may experience a sensor failure. For instance,transportation management system 110 may determine and/or compute aroute to a location (e.g., a location of a service center, such aslocation 191, 192, 193, 194, 195, 196, 197, 198, 199, etc.) based atleast on information associated with the sensor failure. In anotherexample, autonomous vehicle 160 may experience a sensor malfunction. Forinstance, the sensor malfunction may be associated with dirt and/orgrime that has accumulated on and/or within a sensor, and transportationmanagement system 110 may determine and/or compute a route to a location(e.g., a location of a service center, such as location 191, 192, 193,194, 195, 196, 197, 198, 199, etc.) based at least on informationassociated with the sensor malfunction (e.g., a facility that isconfigured to clean dirt or grime off the associated sensor). Inparticular embodiments, determining and/or computing a route to alocation when autonomous vehicle 160 suffers a malfunction and/orfailure may include determining and/or computing a limit of speed and/ora type of road that may be safely navigated based on informationassociated with the malfunction and/or failure. For example, an accuracyof a computer vision system of autonomous vehicle 160 may be affected,and a limit of speed and/or a type of road that may be safely navigatedmay be determined and/or computed based on information associated withthe accuracy of the computer vision system of autonomous vehicle 160.

Turning now to FIG. 2A, examples of various systems are illustrated,according to one or more embodiments. As shown, transportationmanagement system 110 may include an application interface 206, anautonomous vehicle interface 208, a dispatch module 210, a routeselection module 212, an autonomous vehicle monitor module 214, anautonomous data management module 216, and a logistics module 217. Inparticular embodiments, transportation management system 110 may includeone or more data stores. In one example, a data store may include one ormore databases. In another example, a database may include one or moredata stores. As illustrated, transportation management system 110 mayinclude a traffic pattern data store 218, a road condition data store220, an autonomous route data store 222, and facility data store 223. Inparticular embodiments, information stored by one or more of data storesmay be utilized by any of modules 210-217 in performing one or morecorresponding functionalities. Although transportation management system110 is illustrated in a single element, transportation management system110 may be hosted and/or implemented by multiple computer systems and/ordistributed across multiple computer systems, according to particularembodiments. In one example, two or more of modules 210-217 may beperformed and/or implemented by two or more computer systems. In anotherexample, two or more of modules 210-217 may be separated into two ormore services and/or over two or more computer systems to performfunctionalities described herein.

In particular embodiments, transportation management system 110 maycommunicate with one or more requestor computing devices 130, one ormore client computing devices 224, one or more autonomous vehiclecomputing devices 150, and/or one or more other computing devices. Asillustrated, transportation management system 110 may communicate withone or more requestor computing devices 130, one or more clientcomputing devices 224, and one or more autonomous vehicle computingdevices 150 by network 120. In particular embodiments, network 120 mayinclude one or more of a wireless network, a wired network, and anoptical network, among others. For example, network 120 may include oneor more of a wide area network (WAN), a private WAN, a local areanetwork (LAN), a wireless LAN (WLAN), a public switched telephonenetwork (PSTN), a metropolitan area network (MAN), a public WAN (e.g.,an Internet), a satellite telephone network, a cellular telephonenetwork, and a virtual private network (VPN), among others. Inparticular embodiments, network 120 may be coupled to one or more othernetworks. For example, network 120 may be coupled to one or more of aWAN, a WLAN, a PSTN, LAN, a MAN, a public WAN, a private WAN, a cellulartelephone network, a satellite telephone network, and a VPN, amongothers.

In particular embodiments, application interface 206 may include anysoftware and/or hardware configured to send and receive communicationsand/or other information between transportation management system 110and requestor computing devices 130 and/or client computing device 224.For example, application interface 206 may be configured to facilitatecommunication transportation management system 110 and a requestorapplication 228 operating on a requestor computing device 130, which maybe used by a ride requestor 135 to request the transportation managementsystem 110 for a ride. The application interface 206 may alsocommunicate with a data review application 230 operating on a clientcomputing device 224 of a service center, among others. For instance,application interface 206 may receive ride requests, locationinformation, a request location (also referred to as a “pick-up”location, such as location 125A), a drop-off location (such as location125E), a ride type, and/or any other relevant information from requestorcomputing device 130 executing requestor application 228.

In particular embodiments, a ride request may include a requestoridentifier, location information (e.g., an address, latitude andlongitude coordinates, etc.) for requestor computing device 130, apick-up location for the ride request, one or more drop-off locations, apick-up time, and/or any other suitable information associated withproviding a transportation service to a requestor (e.g., a requestor135). In particular embodiments, requestor computing device 130 may be,represent, or include a personal computing device of a user (e.g., aride requestor 135). For example, requestor computing device 130 mayutilized to request a ride service from transportation management system110. In particular embodiments, a ride request may be sent in a singlemessage or may include a series of messages. For example, applicationinterface 206 may receive the ride request and provide ride matchmessages, autonomous vehicle location information, travel routes,pick-up estimates, traffic information, requests for autonomous rideinstructions, autonomous vehicle status information,updates/notifications, and/or any other relevant information torequestor application 228. For instance, requestor application 228 mayreceive the messages from application interface 206 and provide one ormore of visual, audio, and haptic output based at least on the messagesfrom application interface 206.

In particular embodiments, requestor application 228 may be configuredto display, to the requestor (e.g., requestor 135), one or moreavailable routes between the requestor's pickup location and drop-offlocation. The requestor may select one of the one or more availableroutes, causing a message indicating the selected route to be sent totransportation management system 110. In particular embodiments, basedat least on the selected route, dispatch module 210 may dispatch anautonomous vehicle to the pickup location with an instruction to followthe selected route. Route selection module 212 may then updateautonomous route data store 222 to indicate when the route was lasttravelled by an autonomous vehicle 160.

In particular embodiments, autonomous vehicle computing device 150 maybe or include a computing device integrated with an autonomous vehicle(e.g., autonomous vehicle 160), such as an in-vehicle computing deviceconfigured to control the autonomous vehicle. In particular embodiments,autonomous vehicle computing device 150 may be a separate communicationsdevice configured to facilitate communication between transportationmanagement system 110 and the autonomous vehicle.

In particular embodiments, autonomous vehicle interface 208 may includeany software and/or hardware configured to send and receivecommunications and/or other information between transportationmanagement system 110 and autonomous vehicle computing devices 150. Inone example, autonomous vehicle interface 208 may be configured toreceive location information, vehicle and/or ride status information,autonomous vehicle status, and/or any other relevant information fromautonomous vehicle computing device 150. In another example, autonomousvehicle interface 208 may be configured to send ride requests, requestorlocation information, pick-up location information, travel routes,pick-up estimates, traffic information, provider updates/notifications,autonomous vehicle operating instructions, autonomous vehicle data,autonomous vehicle sensor data, and/or any other relevant information tothe autonomous vehicle computing device 150. In particular embodiments,autonomous vehicle computing device 150 may be an in-vehicle computingdevice, such as any computing device that is configured to communicatewith transportation management system 110 and/or requestor computingdevice 130 over one or more communication networks.

For example, autonomous vehicle computing device 150 may include anautonomous vehicle communication module 232 that is configured to managecommunications with transportation management system 110 and/or otherautonomous vehicle computing devices. In particular embodiments,autonomous vehicle communication module 232 may provide vehicle,location, and travel data to transportation management system 110. Inparticular embodiments, autonomous vehicle computing device 150 maycommunicate directly with other nearby autonomous vehicle computingdevices 150 to share location data and/or travel data.

In particular embodiments, travel data may be collected by autonomousvehicle status monitor 234. For example, autonomous vehicle statusmonitor 234 may record information associated with utilization of anautonomous vehicle. For instance, vehicle status monitor 234 may recordone or more of a number of rides completed by the autonomous vehicle, anumber of miles traveled, a time elapsed, and other travel informationsince the autonomous vehicle last received maintenance. In particularembodiments, vehicle maintenance codes, such as codes associated with acheck engine light, oil pressure, oil level, fuel level, etc. may alsobe recorded by autonomous vehicle status monitor 234. For example,autonomous vehicle information may be collected from the autonomousvehicle itself (e.g., by a controller area network bus) or fromapplication programming interfaces provided by a vehicle manufacturer,which may send data directly to an in-car console and/or totransportation management system 110.

In particular embodiments, location module 236 may implement a globalpositioning system (GPS) receiver device, cellular communicationstriangulation, and/or any other suitable location-based techniques todetermine coordinates or locations of device autonomous vehiclecomputing device 150. In particular embodiments, autonomous vehiclecomputing device 150 may request service work based on data recorded byvehicle status monitor 234. For example, a request for service work maybe sent to transportation management system 110. For instance,transportation management system 110 may determine a service center(e.g., a service center 180, a service center 181, or a service center182) to provide service work for the autonomous vehicle.

In particular embodiments, autonomous vehicle computing device 150 maydetermine and/or compute a route to a location when its autonomousvehicle 160 suffers a malfunction and/or failure. In one example,autonomous vehicle 160 may experience a sensor failure. For instance,autonomous vehicle computing device 150 may determine and/or compute aroute to a location based at least on information associated with thesensor failure. In another example, autonomous vehicle 160 mayexperience a sensor malfunction. For instance, the sensor malfunctionmay be associated with dirt and/or grime that has accumulated on and/orwithin a sensor, and autonomous vehicle computing device 150 maydetermine and/or compute a route to a location based at least oninformation associated with the sensor malfunction. In particularembodiments, determining and/or computing a route to a location whenautonomous vehicle 160 suffers a malfunction and/or failure may includedetermining and/or computing a limit of speed and/or a type of road thatmay be safely navigated based on information associated with themalfunction and/or failure. For example, an accuracy of a computervision system of autonomous vehicle 160 may be affected, and a limit ofspeed and/or a type of road that may be safely navigated may bedetermined and/or computed based on information associated with theaccuracy of the computer vision system of autonomous vehicle 160.

In particular embodiments, a service provider may utilize a clientcomputing device 224 to sign-up to provide service to autonomousvehicles. For example, a service provider or a representative of aservice provider may utilize an application (APP) to sign-up to provideservice to autonomous vehicles. For instance, utilizing the APP, theservice provider or the representative of a service provider mayindicate one or more of a capacity (e.g., per thirty minute slots),service status, and compensation (e.g., pricing) for service work, amongothers. As illustrated, client computing device 224 may include aservice request application 240. For example, service requestapplication 240 may notify transportation management system 110 ofavailability, service type(s), etc. For instance, a service center maybe specialized to perform various service types (e.g., autonomousvehicle maintenance, vehicle maintenance, body repair, battery charging,wet cleaning, dry cleaning, etc.). Transportation management system 110may utilize information from client computing device 224 in matching anautonomous vehicle to a service center.

In particular embodiments, service center data may be maintained in aservice center data store 223. When a service center is matched to anautonomous vehicle, information relevant to that service center may beretrieved and/or accessed from service center data store 223. Forexample, if an autonomous vehicle is matched to service center 180B,location information of location 194 (e.g., an address, a latitude and alongitude, etc.) of service center 180B, a size of service center 180B,and/or availability data may be retrieved from service center data store223. Similarly, service center data may be looked up for a batterycharging, cleaning, and/or a maintenance facility, such as size,location, services provided, etc. For example, a battery chargingfacility may be associated with data describing the type of batterycharging available (e.g., standard charger, fast charger, etc.) and typeof battery charging station (e.g., manual or automatic).

In particular embodiments, service center data may also include averagecharge times for different types of autonomous vehicles, how long atypical charge will last in different autonomous vehicles, and/or acharge history for autonomous vehicles that have used one or morespecific charging stations. In a similar fashion, a service center maybe associated with data describing the types of cleaning services, andtypical cleaning times.

In particular embodiments, sensors 238 may include one or more sensors,or sensor arrays, used to identify objects around an autonomous vehicle,as well as a roadway, a lane, a direction, a location, and other objectsand roadway conditions the autonomous vehicle may encounter. In oneexample, sensors 238 may include electromagnetic sensors, includingRADAR, LiDAR, infrared, ultraviolet, optical, and other sensors,acoustic sensors, position sensors, and other sensors. In a secondexample, sensors 238 may include multi-axis accelerometers and/orgyroscopes, weight scales, moisture sensors, in-vehicle cameras, and/orother sensors configured to monitor the interior status, contents,and/or motion of the autonomous vehicle. For instance, accelerometersmay measure motion of the autonomous vehicle's cabin as the autonomousvehicle travels on different roads or different parts of a single road.In another example, sensors 238 may include various sensors describedherein.

In particular embodiments, scales may be used to monitor seats, floors,and/or other user-accessible areas of the autonomous vehicle's cabinand/or may detect whether the weight of the cabin has changed, which mayindicate a potential lost item left behind by a passenger or a potentialspill. The sensor data may be stored in traffic pattern data store 218and/or road condition data store 220. For example, the sensor data maybe analyzed by client computing device 224. Although shown as distinctdata stores, in particular embodiments, traffic pattern data and roadcondition data, along with raw sensor data, location data, and/or anyother type of data gathered by transportation management system 110 maybe stored and/or maintained by a single data store, such as atransportation management system data store.

In particular embodiments, dispatch module 210 may include a softwaremodule that may be configured to process ride requests, ride responses,and/or other communications between ride requestors and providers oftransportation management system 110 to match a requestor and a providerfor a requested transportation service. For example, a ride request maybe received from requestor computing device 130, the ride request caninclude a pickup location and one or more destination and/or drop-offlocations.

In particular embodiments, dispatch module 210 may be configured todetermine a dispatch type for a ride request based at least on one ormore criteria associated with a route and/or a ride requestor. Forexample, the ride request may be originating in an area not served byautonomous vehicles, or the ride requestor's account may be associatedwith preference data indicating that human-driven vehicles should bepreferentially dispatched whenever possible. For instance, dispatchmodule 210 may send an instruction to an autonomous vehicle computingdevice 150 associated with an autonomous vehicle to go to a pickuplocation based at least on the one or more criteria associated with theroute and/or the ride requestor. In particular embodiments, a particularroute may be determined by route selection module 212. For example,route selection module 212 may identify one or more autonomous vehicleroutes from autonomous route data 222 to use based at least on currenttraffic, weather, and/or other roadway conditions. Additionally, oralternatively, route selection module 212 may be configured to select adefault route for an autonomous vehicle based at least on how recentlydata was collected for that route.

In particular embodiments, one or more routes to a service center may bedetermined by route selection module 212. For example, if an autonomousvehicle experiences a service related issue (e.g., a sensor malfunction,a sensor failure, low tire pressure, oil pressure drop, low batterycharge, etc.), route selection module 212 module may determine a routeto a service center. For instance, route selection module 212 module maydetermine a route to a service center that was matched to the autonomousvehicle by logistics module 217. In particular embodiments, routeselection module 212 may determine one or more operational restrictionsthat may be utilized by the autonomous vehicle as the autonomous vehicletravels the route to the service center. For example, the one or moreoperational restrictions may include a limit of speed, a directiveand/or rule not to utilize a highway and/or freeway, and/or come to acomplete before crossing a railroad crossing, among others. Inparticular embodiments, route selection module 212 may determine a routeto a service center based on one or more service related issues of theautonomous vehicle. For example, the autonomous vehicle may not be ableto execute a right-hand turn, and route selection module 212 maydetermine a route to the service center that includes only left-handturns. In one instance, the autonomous vehicle may have a mechanicalmalfunction that prevents the autonomous vehicle from executingright-hand turns. In another instance, the autonomous vehicle may have asensor malfunction that prevents autonomous vehicle computing device 150from determining that an execution of a right-hand turn is safe for theautonomous vehicle and/or others (e.g., pedestrians, other vehicles,etc.).

In particular embodiments, one or more autonomous vehicle routes may bedefined in data store 222. For example, these autonomous vehicle routesmay be defined from designated pickup and drop-off locations in ageographic area. If the pickup and drop-off locations received in theride request are each within one or more threshold distances of thedesignated pickup and drop-off locations, the autonomous vehicle ridetype may be presented as an option to ride requestor 135. Additionally,or alternatively, autonomous vehicle regions may be defined in datastore 222 for a geographic region. For example, each autonomous regionmay be associated with mapping, driving, and/or roadway conditions thatallow autonomous vehicles to navigate between locations within theregion.

In particular embodiments, autonomous vehicle monitor module 214 mayrequest vehicle status information from each autonomous vehiclecomputing device 150. When an autonomous vehicle has completed a ride,autonomous vehicle monitor module 214 may determine if the autonomousvehicle may be made available for additional rides or if the autonomousvehicle needs service work. For example, autonomous vehicle monitormodule 214 can maintain status thresholds and/or status rules. Inparticular embodiments, one or more status thresholds may be defined forvarious metrics collected by vehicle status monitor 234. For example,the one or more status thresholds may include one or more of a drivingtime, a number of rides, and a number of miles, among others.

In particular embodiments, the autonomous vehicle status informationreceived from vehicle status monitor 234 may be compared to the one ormore thresholds. If one or more metrics have exceeded a threshold, theautonomous vehicle may be routed to a service center. Additionally, oralternatively, status rules may be defined for vehicle maintenancecodes, such as check engine codes, tire pressure codes, and oil levelcodes, among others. If a maintenance code is sent from the vehiclestatus monitor 234, it may be compared to the status rules and, if themaintenance code satisfies one or more of the rules, the autonomousvehicle may be routed to a service center. For example, each maintenancecode may be associated with a different value indicating if service workneeds to be performed immediately or if the service work may be deferred(e.g., “high,” “medium,” or “low,” a numerical 1-10, or other value(s)).

In particular embodiments, autonomous vehicle monitor module 214 maydetermine service work needs across the current fleet of autonomousvehicles and may determine if one or more autonomous vehicles need to berouted for service work. For example, if a number of autonomous vehiclesthat are currently undergoing service work is high, and a maintenancecode is associated with a “low” value, the service work may be deferreduntil service work has been completed on other autonomous vehicles. Inparticular embodiments, autonomous vehicle status monitor 234 may beconfigured to request cleaning services based on data collected bysensors 238. For example, if sensors 238 detect vehicle conditionsindicating a cleaning issue, such as a spill, vehicle status monitor 234may send a request to transportation management system 110 indicatingthat the autonomous vehicle requires cleaning. For example, the requestmay include a cleaning type, such as wet cleaning or dry cleaning.Logistics module 217 may determine a service center that provides anappropriate cleaning service, and dispatch module 210 may dispatch theautonomous vehicle to the determined service center.

In particular embodiments, autonomous data management module 216 mayrequest sensor and/or location data from each autonomous vehiclecomputing device 150 of each autonomous vehicle 160. Autonomous datamanagement module 216 may correlate vehicle location and/or speed ofeach corresponding autonomous vehicle 160 and/or may store the data intraffic pattern data store 218. In particular embodiments, accelerometerdata may be correlated and/or associated with location data and/or maybe stored in road condition data store 220. In particular embodiments,one or more data review applications 230 may access traffic pattern datastore 218 and/or road condition data store 220. For example, trafficpattern data and/or road condition data may be mapped and/or visualizedover one or more periods of time. As more data is received fromautonomous vehicle computing devices 150 over time, traffic pattern dataand/or road condition data may vary, as patterns and/or conditionschange. In particular embodiments, recently collected data may beweighted relative to older collected data, which may cause recentlydetected changes to patterns and/or conditions to more quickly replacethe patterns and conditions determined based at least on older data.

In particular embodiments, logistics module 217 may maximizeavailability of autonomous vehicles to ride requestors. For example,logistics module 217 may access information of one or more of datastores 218-223 and may utilize the information to maximize availabilityof autonomous vehicles to ride requestors 135. In particularembodiments, logistics module 217 may access historic data associatedwith supply and demand for autonomous vehicles in a fleet of autonomousvehicles. For example, the historic data for autonomous vehicles may bespecific to a region in which autonomous vehicles 160 operate or toparticular locations within the region in which autonomous vehicles 160operate, or may be specific to autonomous vehicles 160 with particularcharacteristics.

In particular embodiments, logistics module 217 may receive dataassociated with current conditions and/or future events that are likelyto affect demand for autonomous vehicles in the future. For example,logistics module 217 may receive data associated with a future eventthat may be a known as an upcoming event that is likely to affect demandin a region in which fleet of autonomous vehicles 160 operate, such as aconcert, sporting event, or storm event, among others. In particularembodiments, logistics module 217 may receive data associated with anupcoming event that is predicted to take place based on machine learnedpatterns of events that affect demand in a region in which autonomousvehicles 160 operate. For example, a time and duration of rush hours onparticular days may be predicted based on machine learned patterns oftraffic. For instance, the machine learned patterns of traffic may bedetermined from traffic pattern data store 218 and/or road conditiondata store 220, among others.

In particular embodiments, autonomous vehicle monitor module 214 mayreceive data associated with a status of autonomous vehicle 160 and/orone or more of service centers 180, 181, and 182 and/or associated witha condition affecting traffic in the region in which autonomous vehicles160 operate. For example, autonomous vehicle monitor module 214 mayreceive status information from the autonomous vehicles 160 and/or oneor more of service centers 180, 181, and 182 and/or may receivenotifications about current events (such as traffic accidents, weatherevents, road construction, etc.) from autonomous vehicle computingdevices 150, from one or more of service centers 180, 181, and 182,and/or from third parties or external services (such as news services,weather services, government information services, etc.).

In particular embodiments, logistics module 217 may generate aprediction of demand for autonomous vehicles 160, including a predicteddemand level and/or a predicted duration of the predicted demand level,based at least on historic data and received data. Generating theprediction of demand may be further based at least on respectivecharacteristics of one or more autonomous vehicles 160 and/or may befurther based at least on the capabilities, available capacity, speed ofoperations, and/or locations of service centers 180, 181, and/or 182. Inparticular embodiments, the generated prediction of demand may include arespective predicted demand level for each of multiple locations withina region in which autonomous vehicles 160 operate and/or for autonomousvehicles 160 associated with and/or having particular one or morecharacteristics. For example, a generated prediction of demand for aluxury vehicle may be quite different from a generated prediction ofdemand for a vehicle having built-in child safety seats, and thesegenerated predictions of demand may vary at different locations within aregion, at different times of day, and/or on different days (e.g., workdays, weekend days, holidays, etc.).

In particular embodiments, autonomous vehicle monitor module 214 maydetermine that an autonomous vehicle 160 is in need of service, and/orlogistics module 217 may identify a service center suitable forservicing autonomous vehicle 160 and/or may determine a time at whichautonomous vehicle 160 is to be serviced at the identified servicecenter, based at least on the prediction of demand, after whichlogistics module 217 may schedule autonomous vehicle 160 for servicework. For example, logistics module 217 may be configured to determine(e.g., based at least on a status and/or other data received fromautonomous vehicle 160) that autonomous vehicle 160 is due for periodicmaintenance and/or inspection, that a battery of autonomous vehicle 160needs to be charged, that autonomous vehicle 160 needs to be refueled,and/or that autonomous vehicle 160 is in need of one or more repairs,among others.

In particular embodiments, logistics module 217 may identify and/ormatch a service center that is suitable for servicing autonomous vehicle160, which may be based at least on particular service work to beperformed on autonomous vehicle 160 and/or based at least on one or morecapabilities of respective service centers to perform the service work.For example, autonomous vehicle monitor module 214 may receiverespective status information from one or more of service centers 180,181, and/or 182 including capability information.

In particular embodiments, logistics module 217 may compute a score fora service center based at least on one or more attributes associatedwith the service entity. For example, the one or more attributes mayinclude one or more of predicted amounts of wait time for service work,actual amounts of wait time for service work, predicted amounts of timefor service work, actual amounts of time for service work, accuracies ofpledged availabilities for service work, accuracies of pledged speed forservice work (e.g., how fast service work is performed), and whetherproblems persist, among others. In particular embodiments, the one ormore attributes may be weighted. For example, a first attribute of theone or more attribute may be weighted greater than a second attribute ofthe one or more attributes. For instance, attributes may be given valueswith respect to one-another in computing the computed score for aservice entity. In particular embodiments, service entities may utilizea computer system to sign-up to provide service to autonomous vehicles.For example, service entities may utilize a user interface (UI) 242, ofclient computer device 224, to sign-up to provide service to autonomousvehicles. In one instance, a company may utilize a UI 242A, illustratedin FIG. 2B. In another instance, an individual may utilize a UI 242B,illustrated in FIG. 2C.

In particular embodiments, a selection between an internal servicecenter (e.g., a service center that is owned and/or operated by anentity that owns and/or operates the autonomous vehicles) and athird-party service provider may be based on one or more of a value, acost metric (e.g., a partner center may be preferable if a threshold ismet), wait times for service work, availability for service work, aninventory, and a location, among others. For example, logistics module217 may match an autonomous vehicle that needs service work with aninternal service center or a third-party service center based at leaston these attributes, among others.

In particular embodiments, logistics module 217 may match a servicecenter with an autonomous vehicle based at least on features of ageographic region. In one example, a feature of a geographic region mayinclude a density. In one instance, a density may include a populationdensity, which may affect one or more traffic patterns at one or moretimes of a day. In another instance, a density may include a roaddensity, which may affect one or more traffic patterns (e.g., a greaterroad density may permit traffic to move more expediently than a lesserroad density, which may not have as many roads per unit of area). Inanother example, a feature of a geographic region may include one ormore traffic patterns. In one instance, one or more traffic patterns maybe associated with respective one or more times of a day (e.g., morningrush hour, lunch hour, evening rush hour, etc.). In a second instance,one or more traffic patterns may be associated with respective one ormore times days of a week (e.g., week days, weekend days, etc.). In athird instance, one or more traffic patterns may be associated withrespective one or more holidays (e.g., Christmas holidays, governmentholidays, etc.). In another instance, one or more traffic patterns maybe associated with respective one or more festivals/conventions (e.g., afestival of lights, Austin City Limits (ACL) Festival, South bySouthwest (SXSW) festival, a business convention, a parade, etc.).

In particular embodiments, logistics module 217 may match a servicecenter with an autonomous vehicle based at least on one or more servicecenter models. In one example, a service center model may include alarge, medium, or small service center model. In one instance, a large,medium, or small service center model may be associated with respectivesizes of inventories of parts for different types of vehicles (e.g., alarge service center model may be associated with a large inventory ofparts for different types of vehicles than a small service centermodel). In a second example, a service center model may include a mobileservice center model. For instance, a mobile service center model may beassociated with one or more abilities to travel to different locationsto service autonomous vehicles. In another example, a service centermodel may include a distributed service center model. In one instance, adistributed service center model may include service centers that aredistributed throughout a geographic region. In another instance, adistributed service center model may include service centers that aredistributed throughout a geographic region and provide differentservices, based at least on their respective locations.

In particular embodiments, logistics module 217 may match a servicecenter with an autonomous vehicle based at least on an availablecapacity of a service center. In one example, a service center may havea current available capacity to service the autonomous vehicle. Inanother example, a service center may have a future available capacityto service the autonomous vehicle. For instance, the service center mayhave available capacity to service the autonomous vehicle by a time theautonomous vehicle arrives at a location of the service center.

In particular embodiments, logistics module 217 may match a servicecenter with an autonomous vehicle based at least on one or more currentattributes of the autonomous vehicle. In one example, a currentattribute of the autonomous vehicle may include a range of theautonomous vehicle. In one instance, the range may be a maximum amountof distance the autonomous vehicle may travel with an amount of storedbattery charge. In another instance, the range may be a maximum amountof distance the autonomous vehicle may travel with an amount of storedfuel. In a second example, a current attribute of the autonomous vehiclemay include a maximum speed that the autonomous vehicle may travel. Inone instance, autonomous vehicle computing device 150 of the autonomousvehicle may determine the maximum speed that the autonomous vehicle maytravel. In another instance, transportation management system 110 maydetermine the maximum speed that the autonomous vehicle may travel. Inanother example, a current attribute of the autonomous vehicle mayinclude an ability to travel a distance during current weatherconditions or future weather conditions. For instance, if a futureweather condition includes rain, an ability to travel a distance duringrain may be limited or non-existent.

In particular embodiments, logistics module 217 may determine a time atwhich autonomous vehicle 160 is to be serviced further based at least ona characteristic of autonomous vehicle 160, one or more capacities ofvarious service centers to perform the service work at particular times,an estimated amount of time required to perform the service work at theone or more of service centers 180, 181, and/or 182, and/or estimates ofa travel time from a current location of autonomous vehicle 160 to theone or more of service centers 180, 181, and/or 182. In particularembodiments, determining the time at which the given autonomous vehicleis to be serviced may include determining, based at least on thegenerated prediction of demand, a default interval between serviceappointments for multiple autonomous vehicles 160 and/or determining,based at least on the generated prediction of demand, an order in whichthe multiple autonomous vehicles 160 of autonomous vehicles are to beserviced. In particular embodiments, logistics module 217 may provideinstructions to autonomous vehicle 160 to drive to a determined servicecenter for scheduled service work at a determined service time. In oneexample, logistics module 217 may provide instructions 155A toautonomous vehicle 160E to drive to service center 180A.

In another example, logistics module 217 may provide instructions 155Cto autonomous vehicle 160G to drive to service center 181B. Inparticular embodiments, logistics module 217 or another portion oftransportation management system 110 may be configured to communicatewith autonomous vehicle 160 to provide navigation instructions and/orother commands to the vehicle, as described in further detail below. Inparticular embodiments, logistics module 217 may provide, to clientcomputing device 217, service information may include access informationand/or authorization information that may be utilized by the servicecenter to access the autonomous vehicle and/or service the autonomousvehicle. In particular embodiments, a service center may be grantedand/or provided with an authorization to access and/or operate anautonomous vehicle. In one example, the service center may be grantedand/or provided with an ability to unlock the autonomous vehicle. In asecond example, the service center may be granted and/or provided withan ability to drive the autonomous vehicle. In another example, theservice entity may be granted and/or provided with an ability to operatethe autonomous vehicle. In particular embodiments, an authorization toaccess and/or operate an autonomous vehicle may be based at least on oneor more of a time period and/or one or more locations. In one example,the service center may be granted and/or provided with an authorizationto access and/or operate during one or more time periods. In anotherexample, the service center may be granted and/or provided with anauthorization to access and/or operate at one or more locations. Forinstance, the authorization to access and/or operate the autonomousvehicle may be location-based.

In particular embodiments, logistics module 217 may determine one ormore sequences of service works to be performed on autonomous vehicles160. For example, determining one or more sequences of service works tobe performed on autonomous vehicles 160 may aid and/or assist inmaximizing availability of autonomous vehicles to ride requestors 135.In particular embodiments, logistics module 217 may access informationof service center data store 223 and utilize one or more of aconfiguration of the determined service center, a current availabilityof charging stations of the determined service center, a futureavailability of charging stations of the determined service center, await time for service work, a rate for service work, a capacity forservice work, a number of service work types offered, an availabilityfor service work, operator cost (e.g., labor, parts, storage, realestate, etc.), an inventory of one or more items for service work,predicted amounts of wait time for service work, predicted amounts oftime for service work, a number of autonomous vehicles currently beingserviced, and a number of autonomous vehicles that are scheduled to beserviced, among others, in determining one or more sequences of serviceworks to be performed on autonomous vehicles 160.

In particular embodiments, transportation management system 110 mayprovide compensation information, associated with service workinformation, to one or more service entities. For example,transportation management system 110 may provide one or morecompensations (e.g., pricing) respectively associated with one or moreservice works to be performed on an autonomous vehicle. In particularembodiments, transportation management system 110 may vary and/or alterthe compensation information at one or more times. In one example, if aservice center has not responded to a request for service work to beperformed, within an amount of time transpiring, transportationmanagement system 110 may increase an amount of compensation (e.g., anamount of money, an amount of credits, an amount of crypto-currency, anamount of currency, etc.) associated with the service work to beperformed. For instance, transportation management system 110 mayincrease the amount of compensation associated with the service work tobe performed until transportation management system 110 receives aresponse from a service entity to perform the service work requested. Inanother example, if multiple service entities reply and/or respond to arequest for service work to be performed, transportation managementsystem 110 may decrease an amount of compensation associated with theservice work to be performed for one or more service works to beperformed.

In particular embodiments, transportation management system 110 mayinclude a demand model generation system, which may be configured toaccess historic supply and demand data, receive data 170 from servicecenter(s) 180 and/or 181, receive data 165 from one or more autonomousvehicles 160, and generate a prediction of demand for autonomousvehicles 160 based on these inputs. For example, data 170A and/or 170Bfrom service center(s) 180 and/or data 170C and/or 170D from servicecenter(s) 181 may include data representing service center status,capabilities, estimated times for performing particular services (e.g.,estimated amounts of time for service work), location information,available capacity for service work, and types of cleaning services,among others. As another example, data 165 received from autonomousvehicles 160 may include vehicle sensor data, vehicle statusinformation, such as mileage and/or a time interval since the autonomousvehicle was last serviced, and/or data indicating that the autonomousvehicle is in need of fuel, a battery charge, an oil change, a car wash,and/or a repair to fix or replace a broken sensor and/or address awear-and-tear condition, among others.

In particular embodiments, transportation management system 110 maymatch autonomous vehicles to service center(s) 180, 181, and/or 182based at least on historic data. For example, the historic data mayinclude, for each service center, one or more of measured wait times forservice work to be performed, past predicted wait times for service workto be performed, measured times for service work that was performed,past predictions of amounts of time for service work that was performed,and past parts inventories, among others. For instance, the historicdata may be utilized in computing a score for each service center.

In particular embodiments, a computed score for a service center may bebased at least on one or more attributes associated with the servicecenter. For example, the one or more attributes may include one or moreof predicted amounts of wait time for service work, amounts of wait timefor service work, predicted amounts of time for service work, amounts oftime for service work, accuracies of pledged availabilities for servicework, accuracies of pledged speed for service work (e.g., how fastservice work is performed), and if problems persist, among others. Inparticular embodiments, the one or more attributes may be weighted. Forexample, a first attribute of the one or more attribute may be weightedgreater than a second attribute of the one or more attributes. Forinstance, attributes may be given values with respect to one-another incomputing the score for a service center.

In particular embodiments, valuing attributes with respect toone-another in computing the score for a service center may includevaluing a first attribute of the one or more attribute associated withone or more repairs for an issue greater than a second attribute of theone or more attribute associated with one or more symptomatic proceduresfor the issue. For example, an issue may include low tire pressure. Inone instance, the first attribute may be associated with a first servicecenter that may repair a leak in a tire, while the second attribute maybe associated with a second service center that may add air to the tire.In another instance, the first attribute may be associated with a firstservice center that may replace a tire, while the second attribute maybe associated with a second service center that may add air to the tire.In particular embodiments, valuing attributes with respect toone-another in computing the score for a service center may includevaluing a first attribute of the one or more attribute associated withone or more root-cause repairs for an issue greater than a secondattribute of the one or more attribute associated with one or moresymptomatic treatments for the issue. For example, a tire may bedamaged, and adding air to the tire may be a symptomatic treatment,while repairing a leak of the damage tire or replacing the damaged tiremay be a root-cause repair for the damaged tire. For instance, a firstattribute may be associated with a first service center that may addressan underlying issue (e.g., root-cause) may be valued greater than asecond attribute may be associated with a second service center that mayaddress a symptom of the underlying issue.

In particular embodiments, valuing attributes with respect toone-another in computing the score for a service center may includevaluing a first attribute of the one or more attribute associated with afirst service center that services a particular make of an autonomousvehicle greater than a second attribute of the one or more attributeassociated with a second service center that provides general servicesfor autonomous vehicles. In particular embodiments, valuing attributeswith respect to one-another in computing the score for a service centermay include utilizing data that is publicly available. In one example, afirst service center may have received more favorable reviews than asecond service center, based at least on the data that is publiclyavailable, and a first attribute may be associated with the firstservice center may be valued greater than a second attribute of the oneor more attribute associated with the second service center. Forinstance, the first service center may have received more favorablereviews on YELP than the second service center. In another example, afirst service center may have received more affirmative affinities thana second service center, based at least on the data that is publiclyavailable, and a first attribute may be associated with the firstservice center may be valued greater than a second attribute of the oneor more attribute associated with the second service center. Forinstance, the first service center may have received more “Likes” onFACEBOOK than the second service center.

Turning now to FIG. 2B, a UI 242A is illustrated, according to one ormore embodiments. As shown, UI 242A may include text input areas 250 andcheck buttons 252. In particular embodiments, UI 242A may be utilized tosign up third-party service work. As illustrated, for example, UI 242Amay be utilized to sign up a company, “Tuxedo Automotive Care, LLC”. Forinstance, a company/individual name, an address, a city/state/zip, anumber of charging stations, and a number of fueling stations may beinput into respective text input areas 250A-250E.

In particular embodiments, a check input button 252 may indicate a “yes”or “no” input. For example, check buttons 252A-252H may indicate a “yes”or “no” input for a dry vacuuming service, a wet vacuuming service, acar wash service, a sensor service, an oil change service, a windowcleaning service, a mobile service, and a fluid replacement service(e.g., windshield washer fluid, coolant fluid, etc.), respectively. Inone instance, as illustrated, the third-party, “Tuxedo Automotive Care,LLC”, may offer a dry vacuuming service, a wet vacuuming service, a carwash service, an oil change service, a window cleaning service, and afluid replacement service. In another instance, as shown, thethird-party, “Tuxedo Automotive Care, LLC”, may not offer a sensorservice and a mobile service. In particular embodiments, a submissionbutton/icon 254 may be utilized and/or actuated to submit sign upinformation of UI 242A to transportation management system 110.

Turning now to FIG. 2C, a UI 242B is illustrated, according to one ormore embodiments. As shown, UI 242B may include text input areas 250 andcheck buttons 252. In particular embodiments, UI 242B may be utilized tosign up third-party service work. As illustrated, for example, UI 242Bmay be utilized to sign up an individual, “John Smith”. For instance, acompany/individual name, an address, a city/state/zip, a number ofcharging stations, and a number of fueling stations may be input intorespective text input areas 250A-250E.

In particular embodiments, a check input button 252 may indicate a “yes”or “no” input. For example, check buttons 252A-252H may indicate a “yes”or “no” input for a dry vacuuming service, a wet vacuuming service, acar wash service, a sensor service, an oil change service, a windowcleaning service, a mobile service, and a fluid replacement service(e.g., windshield washer fluid, coolant fluid, etc.), respectively. Inone instance, as illustrated, the third-party, “John Smith”, may offer adry vacuuming service, a car wash service, a window cleaning service,and a mobile service. In another instance, as shown, the third-party,“John Smith”, may not offer a wet vacuuming service, a sensor service,an oil change service, and a fluid replacement service. In particularembodiments, a submission button/icon 254 may be utilized and/oractuated to submit sign up information of UI 242B to transportationmanagement system 110.

In particular embodiments, a service provider may be screened. Forexample, the service provider may be a person, and the person may bescreened. For instance, screening the service entity may includeperforming a background check on the person, analyzing a past workhistory, and analyzing a past history with a ride service provider,among others. In particular embodiments, when an autonomous vehicleneeds to be serviced, suitable service providers (e.g., serviceindividuals) may be determined based at least on a service needed, askill set, a location of the service provider, and an availability ofthe service provider, among others. For example, after determining oneor more service individuals, the one or more service individuals may benotified by one or more of an APP, a text message (e.g., a short messageservice (SMS) message), and an email, among others. For instance, theone or more service individuals may reply to a notification with anacceptance or a declination to provide service work for one or moreautonomous vehicles. In particular embodiments, after a serviceindividual indicates an acceptance to provide service work, anautonomous vehicle may be instructed by transportation management system110 to drive to a service center of the service individual.

Turning now to FIG. 3, an example method 300 for determining a score ofa service center is illustrated, according to one or more embodiments.At step 310, data associated with a service center may be received. Inparticular embodiments, the data associated with the service center mayinclude one or more data attributes. For example, the one or more dataattributes may include one or more of a predicted wait time for aservice work instance, an actual wait time for the service workinstance, a rate (e.g., a speed) for the service work instance, a numberof items in an inventory of the service center, a number of chargingstations, location information associated with the service center, anoffered service type (e.g., oil change, battery charging, petrolfueling, wet vacuuming, dry vacuuming, sensor replacement, etc.), anumber of service bays, a price for a service type, and a number ofproblems that have persisted after service work, among others.

In particular embodiments, the data associated with a service center maybe received from one or more sources. In one example, a portion of thedata may be received from service center data store 223. In a secondexample, a portion of the data may be received from the service center.In one instance, one or more predicted times may be received from theservice center. In a second instance, one or more actual times may bereceived from the service center. In a third instance, one or more of anumber of items in an inventory of the service center, a number ofcharging stations, location information associated with the servicecenter, an offered service type, a number of service bays, and a pricefor a service type, among others, may be received from the servicecenter. In another example, a portion of the data may be received fromone or more autonomous vehicles. For instance, one or more actual timesmay be received from the one or more autonomous vehicles.

At step 315, it may be determined if a first data attribute iscomparable with a second data attribute. In one example, a first dataattribute may be or include a predicted wait time for a service workinstance, and a second data attribute may be or include an actual waittime for the service work instance. In another example, a first dataattribute may be or include a predicted amount of time for a servicework instance, and a second data attribute may be or include an actualamount of time for the service work instance. If a first data attributeis comparable with a second data attribute, the first data attribute maybe compared with the second data attribute, at step 320. In one example,comparing the first data attribute the second data attribute may includetaking a difference between the first data attribute and the second dataattribute. In another example, comparing the first data attribute thesecond data attribute may include determining a ratio from the firstdata attribute and the second data attribute.

At step 325, a comparison of the first attribute and the secondattribute may be weighted. In particular embodiments, the comparison ofthe first attribute and the second attribute may be weighted relative toanother comparison of two attributes and/or may be weighted relative toanother attribute. For example, a weight may include a number. Forinstance, the number may be multiplied with the comparison.

In particular embodiments, a weight of a data attribute may bedetermined via a value of a given parameter to one or more of autonomousvehicle utilization and profitability metrics, among others. In oneexample, if autonomous vehicle transportation service is urgently neededduring a high demand period, a weighting of a distance and predictedversus an actual wait time might be higher than that of a price forservicing an autonomous vehicle. In another example, during low demandtime periods a price for servicing an autonomous vehicle might beweighted more highly than distance parameters or wait time parameters,among others. In particular embodiments, a weight of a data attributemay be determined dynamically. In one example, a weight of a dataattribute may be determined dynamically based at least on local orregional demand. In another example, a weight of a data attribute may bedetermined dynamically based at least on an autonomous vehicle fleetstatus. In particular embodiments, a scoring system, method, and/orprocess may be utilized to dynamically determine one or more weights ofrespective one or more data attributes.

At step 330, the weighted comparison may be applied to a score. Inparticular embodiments, the score may be a numerical value. In oneexample, the weighted comparison may be added to the score. Forinstance, the score may start with zero (0). In another example, theweighted comparison may be multiplied with the score. For instance, thescore may start with one (1). In particular embodiments, the method mayproceed to step 350.

If a first data attribute is not comparable with a second dataattribute, it may be determined if the data attribute is applicable, atstep 335. In particular embodiments, one or more data attributes may notbe applicable to determining a score associated with a service provider.In one example, location information may not be applicable todetermining a score associated with a service center. In other examples,applicable attributes may indicate and/or be associated with a fluidreplacement service, an oil change service, a dry vacuuming service, awet vacuuming service, a number of charging stations, a sensor service(e.g., sensor cleaning, sensor replacement, etc.), a capacity for theservice work, a number of service work types offered, operator cost(e.g., labor, parts, storage, real estate, etc.), an inventory of one ormore items for the service work, and/or scores based at least onpredicted and actual amount of time associated with service work, amongothers. In particular embodiments, numerical values may be assigned tothe applicable attributes associated with the service center. If thedata attribute is not applicable, the method may proceed to step 350. Ifthe data attribute is applicable, the data attribute may be weighted, atstep 340. In particular embodiments, the data attribute may be weightedrelative to a comparison of two attributes and/or may be weightedrelative to another attribute. For example, a weight may include anumber. For instance, the number may be multiplied with the dataattribute.

At step 345, the weighted attribute may be applied to a score. In oneexample, the weighted attribute may be added to the score. For instance,the score may start with zero (0). In another example, the weightedattribute may be multiplied with the score. For instance, the score maystart with one (1). At step 350, it may be determined if there isanother attribute. If there is another attribute, the method may proceedto step 315, according to one or more embodiments. If there is notanother attribute, the score may be outputted, at step 355. In oneexample, outputting the score may include displaying the score. Forinstance, the score may be displayed via a user interface. In anotherexample, outputting the score may include storing the score. In oneinstance, the score may be stored in a memory medium. In anotherinstance, the score may be stored in a data store (e.g., service centerdata store 223).

In particular embodiments, a score of a first service center may beutilized with a score of a second service center. For example, the scoreof the first service center may be compared with the score of the secondservice center. For instance, the comparison may indicate if it may bebetter to utilize the first service rather than the second servicecenter. In particular embodiments, scores for multiple service centersmay be utilized to rank the multiple service centers. For example, aservice center with a score that meets or exceeds a threshold value maybe selected to provide service work to an autonomous vehicle.

Particular embodiments may repeat one or more steps of the method ofFIG. 3, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 3 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 3 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method for matchingautonomous vehicles with service entities including the particular stepsof the method of FIG. 3, this disclosure contemplates any suitablemethod for matching autonomous vehicles with service entities includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 3, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 3, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 3.

In particular embodiments, an optimization matching process or methodmay utilize dynamically updated scores for multiple service centers inproviding optimized matches for autonomous vehicles requiring service.For example, the optimization matching process or method may attempt toprovide optimized matches between a service center and an autonomousvehicle (based at least on dynamic scoring) in producing one or moreoutputs. For instance, providing optimized matches between a servicecenter and an autonomous vehicle may be based at least on one or more ofa highest probability of issue resolution on a first visit to a servicecenter, a highest probability of issue resolution by a certain time, anda lowest all-inclusive cost to resolve a service issue (e.g., allinclusive may include cost of travel to service center, cost of waiting,cost of service, cost of vehicle's return to target area, etc.), amongothers.

Turning now to FIGS. 4A and 4B, an example method 400 for operating atransportation management system is illustrated, according to one ormore embodiments. At step 410, it may be determined that an autonomousvehicle is experiencing a service related issue. In particularembodiments, a service related of an autonomous vehicle issue mayinclude an issue or a problem with the autonomous vehicle. For example,the issue or the problem may include a failed sensor, a malfunctioningsensor, a burned out headlight, a burned out taillight, a burned outturn signal light, a burned out cabin light, low tire pressure, a clogor an obstruction in a cabin air distribution system, a blown fuse, abroken window or windshield, a broken or failing air conditioningsystem, an oil leak, a fuel leak, a coolant leak, low oil pressure, or alow level of coolant, among others.

For instance, autonomous vehicle 160B may be experiencing a servicerelated issue. In particular embodiments, determining a service relatedissue may include receiving data from a sensor of autonomous vehicle160. For example, autonomous vehicle computing device 150 and/ortransportation management system 110 may receive that data from thesensor of autonomous vehicle 160 and/or may determine a service relatedissue based at least on the data from the sensor of autonomous vehicle160. In one instance, if a comparison of the data with one or moremetrics exceeds a threshold, autonomous vehicle computing device 150and/or transportation management system 110 may determine that there isthe service related issue.

In another example, if a comparison of the sensor data, from autonomousvehicle 160, with one or more metrics falls below a threshold,autonomous vehicle computing device 150 and/or transportation managementsystem 110 may determine that there is a service related issue. Inparticular embodiments, determining that there is a service relatedissue may include determining that data from a sensor of autonomousvehicle 160 has not been received within an amount of time. For example,if autonomous vehicle computing device 150 and/or transportationmanagement system 110 does not receive data from the sensor ofautonomous vehicle 160 within the amount of time, autonomous vehiclecomputing device 150 and/or transportation management system 110 maydetermine that there is the service related issue. In particularembodiments, if autonomous vehicle computing device 150 detects aservice related issue with its autonomous vehicle 160, autonomousvehicle computing device 150 may notify transportation management system110. For example, autonomous vehicle computing device 150 may provideinformation that a service related issue has been detected and/orinformation associated with a detected service related issue totransportation management system 110 via network 120.

At step 415, it may be determined that the autonomous vehicle istransporting one or more passengers. In one example, determining thatthe autonomous vehicle is transporting one or more passengers mayinclude determining that autonomous vehicle 160 picked up one or moreriders 135 at location 125, and that location information (e.g.,latitude and longitude coordinates) of autonomous vehicle 160 andlocation information of the one or more riders 135 continue to coincide.In another example, determining that the autonomous vehicle istransporting one or more passengers may include utilizing one or moreweight scales, one or more moisture sensors, one or more in-vehiclecameras, and/or one or more other sensors, of autonomous vehicle 160,configured to monitor an interior status and/or contents of autonomousvehicle 160. For instance, one or more of these sensors may indicate, toautonomous vehicle computing device 150 and/or transportation managementsystem 110, that autonomous vehicle 160 is transporting one or morepassengers.

At step 420, it may be determined if the autonomous vehicle can safelytransport the one or more passengers. If the autonomous vehicle cansafely transport the one or more passengers, the autonomous vehicle maytransport the one or more passengers to a drop-off location, at step425. For example, the autonomous vehicle may transport the one or morepassengers to an originally scheduled and/or planned drop-off location.If the autonomous vehicle cannot safely transport the one or morepassengers, commands and/or instructions may be provided to theautonomous vehicle, at step 430, which may cause the autonomous vehicleto navigate to a safe area and/or location. For example, autonomousvehicle 160B may be experiencing a service related issue. For instance,transportation management system 110 may determine that autonomousvehicle 160B cannot safely transport the one or more passengers, andtransportation management system 110 may provide commands and/orinstructions to autonomous vehicle computing device 150 of autonomousvehicle 160B to navigate to safe area and/or location 184.

At step 435, commands and/or instructions may be provided to anotherautonomous vehicle, which may cause the other autonomous vehicle tonavigate to a location at least proximate to the safe area and/orlocation to pick-up the one or more passengers. In particularembodiments, transportation management system 110 may direct, instruct,and/or navigate another autonomous vehicle 160 to a location at leastproximate to the location of autonomous vehicle 160 that is experiencingthe service related issue, so that the one or more passengers mayutilize the other autonomous vehicle 160 to arrive at a destinationassociated with the one or more people (e.g., a drop-off location). Forexample, transportation management system 110 may direct, instruct,and/or navigate autonomous vehicle 160C to a location at least proximateto safe area and/or location 184, so that the one or more passengers mayutilize autonomous vehicle 160C to arrive at a destination associatedwith the one or more people (e.g., a drop-off location, such as location125D, 125E, 125F, etc.).

At step 440, the autonomous vehicle may be matched with a servicecenter. At step 445, it may be determined if the autonomous vehicle cansafely transport itself to the service center. If the autonomous vehiclecannot safely transport itself to the service center, the autonomousvehicle may be secured from further operations, at step 450. In oneexample, securing the autonomous vehicle from further operations mayinclude shutting down and/or turning off one or more systems and/orsubsystems of the autonomous vehicle. In another example, securing theautonomous vehicle from further operations may include having theautonomous vehicle sit idle, until a human and/or another autonomousvehicle can intervene and/or assist the autonomous vehicle.

If the autonomous vehicle can safely transport itself to a servicecenter, a route to the service center may be determined, at step 455. Inparticular embodiments, transportation management system 110 maydetermine and/or compute a route to a location of the matched servicecenter. In one example, autonomous vehicle 160 may experience a sensorfailure. For instance, transportation management system 110 maydetermine and/or compute a route to a location (e.g., a location of aservice center, such as location 191, 192, 193, 194, 195, 196, 197, 198,199, etc.) based at least on information associated with the sensorfailure. In another example, autonomous vehicle 160 may experience asensor malfunction. For instance, the sensor malfunction may beassociated with dirt and/or grime that has accumulated on and/or withina sensor, and transportation management system 110 may determine and/orcompute a route to a location (e.g., a location of a service center,such as location 191, 192, 193, 194, 195, 196, 197, 198, 199, etc.)based at least on information associated with the sensor malfunction.

In particular embodiments, transportation management system 110 maydetermine and/or compute a route to a location of the matched servicecenter based at least on one or more of safety while traveling to thematched service center and success of travel to the matched servicecenter, among others. For example, transportation management system 110may determine and/or compute the route to the location of the matchedservice center that may be otherwise less efficient than another route.For instance, the route to the location of the matched service centermay be determined and/or computed based at least on one or moreperformance limitations of autonomous vehicle 160 and/or circumventingthe one or more performance limitations imposed by a sensorfailure/malfunction or another service-related issue. In particularembodiments, determining and/or computing a route to a location whenautonomous vehicle 160 suffers a malfunction and/or failure may includedetermining and/or computing a limit of speed and/or a type of road thatmay be safely navigated based on information associated with themalfunction and/or failure. For example, an accuracy of a computervision system of autonomous vehicle 160 may be affected, and a limit ofspeed and/or a type of road that may be safely navigated may bedetermined and/or computed based on information associated with theaccuracy of the computer vision system of autonomous vehicle 160. Forinstance, autonomous vehicle 160 may avoid highways and/or freeways.

At step 460, the routed and any operational restrictions may be providedto the autonomous vehicle. For example, transportation management system110 may provide, via network 120, the route and any operationalrestrictions to autonomous vehicle computing device 150 of autonomousvehicle 160. For instance, one or more operational instructions mayinclude a limit of speed at which autonomous vehicle 160 may travel, atype of road that autonomous vehicle 160 may travel, and/or one or moretimes of day that autonomous vehicle 160 may travel, among others.

Particular embodiments may repeat one or more steps of the method ofFIGS. 4A and 4B, where appropriate. Although this disclosure describesand illustrates particular steps of the method of FIGS. 4A and 4B asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method of FIGS. 4A and 4B occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for matching autonomous vehicles withservice entities including the particular steps of the method of FIGS.4A and 4B, this disclosure contemplates any suitable method for matchingautonomous vehicles with service entities including any suitable steps,which may include all, some, or none of the steps of the method of FIGS.4A and 4B, where appropriate. Furthermore, although this disclosuredescribes and illustrates particular components, devices, or systemscarrying out particular steps of the method of FIGS. 4A and 4B, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIGS. 4A and 4B.

Turning now to FIG. 5, an example method 500 for matching autonomousvehicles with service centers is illustrated, according to one or moreembodiments. The method 500 may be at step 510, where data associatedwith an autonomous vehicle may be received. For example, transportationmanagement system 110 may receive data associated with an autonomousvehicle 160. In particular embodiments, the data associated with theautonomous vehicle may include one or more of information that indicatesan issue with the autonomous vehicle, information that indicates aproblem with the autonomous vehicle, and information that indicates ascheduled maintenance of the autonomous vehicle, among others. Forexample, autonomous vehicle computing device 150 may provide the dataassociated with the autonomous vehicle to transportation managementsystem 110.

At step 515, it may be determined, based at least on the data associatedwith the autonomous vehicle, that the autonomous vehicle is to beserviced. For example, transportation management system 110 maydetermine, based at least on the data associated with the autonomousvehicle, that the autonomous vehicle is to be serviced. In particularembodiments, determining that the autonomous vehicle is to be servicedmay include determining that at least a portion of the data associatedwith the autonomous vehicle meets or exceeds a first threshold or meetsor falls below a second threshold. In particular embodiments,determining that the autonomous vehicle is to be serviced may include acomputer system of the autonomous vehicle determining that theautonomous vehicle is to be serviced.

In one example, the data associated with the autonomous vehicle mayinclude a tire-pressure value, and transportation management system 110may determine that the tire-pressure value is below a threshold valueand may determine, based at least on the determination that thetire-pressure value is below the threshold value, that the autonomousvehicle is to be serviced. In a second example, the data associated withthe autonomous vehicle may include a battery charge value, andtransportation management system 110 may determine that the batterycharge value is below a threshold value and may determine, based atleast on the determination that the battery charge value is below thethreshold value, that the autonomous vehicle is to be serviced. In asecond example, the data associated with the autonomous vehicle mayinclude information that indicates a sensor malfunction or failure. In athird example, the data associated with the autonomous vehicle mayinclude information that indicates at least one sensors requirescleaning. In a fourth example, the data associated with the autonomousvehicle may include information that a battery discharge rate is below athreshold value. In another example, the data associated with theautonomous vehicle may include a refrigerant volume value, andtransportation management system 110 may determine that the refrigerantvolume value is below a threshold value and may determine, based atleast on the determination that the refrigerant volume value is belowthe threshold value, that the autonomous vehicle is to be serviced. Forinstance, an ability to cool a passenger compartment of the autonomousvehicle may be based at least on the refrigerant volume value.

At step 520, the autonomous vehicle may be matched with a service centerto service the autonomous vehicle, using the data associated with theautonomous vehicle and a database that stores information associatedwith service entities. For example, transportation management system 110may match the autonomous vehicle, using the data associated with theautonomous vehicle and service center data store 223, with a servicecenter. In particular embodiments, matching the autonomous vehicle withthe service center to service the autonomous vehicle may be based atleast on one or more of a wait time for service work, a rate for theservice work, a capacity for the service work, a number of service worktypes offered, availability, operator cost (e.g., labor, parts, storage,real estate, etc.), transportation disruption risks, traffic patterns,road conditions, environmental factors (e.g., temperature, flooding,rain, snow, sleet, ice, etc.), a range of an autonomous vehicle, a maxspeed of an autonomous vehicle, a weather tolerance of an autonomousvehicle, a day/night functionality of an autonomous vehicle, currentcondition of an autonomous vehicle, a service record of a serviceentity, a scheduled maintenance of an autonomous vehicle, a serviceurgency value for the service work, an inventory of one or more itemsfor the service work, a location for the service work, predicted amountsof wait time for service work, predicted amounts of time for servicework, and scores for respective ones of the service centers, amongothers.

At step 525, location information of the service center may be providedto a computer system of the autonomous vehicle. For example,transportation management system 110 may provide, to autonomous vehiclecomputing device 150, location information of the service center. Forinstance, transportation management system 110 may be configured toprovide navigation instructions and/or other commands 155 to autonomousvehicle 160. In particular embodiments, navigation instructions and/orother commands 155 may include prepositioning instructions indicatinglocations to which the autonomous vehicle 160 should be directedfollowing the matching of autonomous vehicle 160 with the servicecenter. In particular embodiments, the location of the service work maybe or include a location of a service center that was matched to theautonomous vehicle (e.g., matched at step 520). In one example, thelocation information may include an address. In another example, thelocation information may include coordinates. For instance, thecoordinates may include latitude and longitude coordinates. Inparticular embodiments, the location information may indicate a locationof where a mobile service center will be. For example, the locationinformation may indicate a location of where the autonomous vehicle willmeet the mobile service center. For instance, the location of where theautonomous vehicle will meet the mobile service center may be or includea parking lot.

At step 530, service information may be provided to a computer system ofthe service center. For example, transportation management system 110may provide service information a client computing device 224 of thematched service center. In particular embodiments, the serviceinformation may include one or more issues and/or problems associatedwith an autonomous vehicle. In particular embodiments, the serviceinformation may include access information and/or authorizationinformation that may be utilized by the service center to access theautonomous vehicle and/or service the autonomous vehicle.

In particular embodiments, a service center may be granted and/orprovided with an authorization to access and/or operate an autonomousvehicle. In one example, the service center may be granted and/orprovided with an ability to unlock the autonomous vehicle. In a secondexample, the service center may be granted and/or provided with anability to drive the autonomous vehicle. In a third example, the serviceentity may be granted and/or provided with an ability to operate theautonomous vehicle. In another example, the service center may begranted and/or provided with an ability to access and/or modify aconfiguration of a computer system of the autonomous vehicle. In oneinstance, the service center may be granted and/or provided with theability to access and/or modify the configuration of the computer systemof the autonomous vehicle to recalibrate a sensor and/or a sensorcomponent and/or to access and/or modify the configuration for a newlyinstalled sensor. In another instance, the computer system of theautonomous vehicle may be or include autonomous vehicle computing device150.

In particular embodiments, an authorization to access and/or operate anautonomous vehicle may be based at least on one or more of a time periodand/or one or more locations. In one example, the service center may begranted and/or provided with an authorization to access and/or operateduring one or more time periods. In a second example, the service centermay be granted and/or provided with an authorization to access and/oroperate at one or more locations. For instance, the authorization toaccess and/or operate the autonomous vehicle may be location-based. Inanother example, transportation management system 110 may send an accesscode to client computing device 224 of the service center and/or sendinstructions to autonomous vehicle computing device 150, which mayunlock features (e.g., safety features, maintenance access features,etc.) of autonomous vehicle 160 upon a determination that autonomousvehicle 160 has arrived at a location of the service center.

Particular embodiments may repeat one or more steps of the method ofFIG. 5, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 5 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 5 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method for matchingautonomous vehicles with service entities including the particular stepsof the method of FIG. 5, this disclosure contemplates any suitablemethod for matching autonomous vehicles with service entities includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 5, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 5, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 5.

Turning now to FIG. 6, an example method 600 for determining a score forservice centers is illustrated, according to one or more embodiments. Atstep 610, predicted amounts of wait time for service work may bereceived. In particular embodiments, a predicted amount of wait time forservice work may include a predicted amount of time that an autonomousvehicle will wait and/or sit idle, at a service center, before forservice work begins. For example, the autonomous vehicle may arrive theservice center before a member of the service center may provide theservice work and/or before a service area (e.g., a service bay, acharging station, a vacuuming station, etc.) is available for theautonomous vehicle. In particular embodiments, the predicted amounts ofwait time for service work may be received from respective ones of theservice centers. For example, the predicted amounts of wait time forservice work may be received from respective client computing devices224 of the service centers. In particular embodiments, transportationmanagement system 110 may receive the predicted amounts of wait time forservice work.

At step 615, predicted amounts of time for service work may be received.In particular embodiments, a predicted amount of time for service workmay include a predicted amount of time that will transpire while theautonomous vehicle undergoes the service work to be performed. Forexample, a predicted amount of time for service work may include apredicted amount of time that will transpire while the autonomousvehicle undergoes charging, fueling, sensor replacement, dry vacuuming,wet vacuuming, an oil change, etc. In particular embodiments, thepredicted amounts of time for service work may be received fromrespective ones of the service centers. For instance, the predictedamounts of time for service work may be received from respective clientcomputing devices 224 of the service centers. In particular embodiments,transportation management system 110 may receive the predicted amountsof time for service work.

At step 620, amounts of actual wait time for service work may bereceived. In particular embodiments, an actual wait time for servicework may include an actual amount of time that an autonomous vehicle haswaited and/or sat idle, at a service center, before service work hadbegun. In one example, the amounts of actual wait time for service workmay be received from respective ones of the service centers. Forinstance, the amounts of actual wait time for service work may bereceived from respective client computing devices 224 of the servicecenters. In another example, the amounts of actual wait time for servicework may be received from respective autonomous vehicle computingdevices 150 of respective autonomous vehicles 160. In particularembodiments, transportation management system 110 may receive theamounts of actual wait time for service work.

At step 625, actual amounts of time for service work may be received. Inparticular embodiments, an actual amount of time for service work mayinclude an actual amount of time that it has taken for the service workto have been completed (e.g., a time from a start of the service work toa time of a finish of the service work). In one example, the actualamounts of time for service work may be received from respective ones ofthe service centers. For instance, the actual amounts of time forservice work may be received from respective client computing devices224 of respective service centers. In another example, the actualamounts of time for service work may be received from respectiveautonomous vehicle computing devices 150 of respective autonomousvehicles 160. In particular embodiments, transportation managementsystem 110 may receive amounts of time for service work.

In particular embodiments, a predicted amount of time for service workand an actual amount of wait time may be zero (0). In one example, aservice center may reserve one or more service personnel and/or one ormore service bays/areas, such that there is no wait time. In anotherexample, a service center may prioritize one or more service personneland/or one or more service bays/areas, such that there is no wait time.

In particular embodiments, steps 630-640 may be performed for eachservice center. At step 630, predicted times may be compared with actualtimes. In one example, each predicted amount of wait time for servicework may be compared with each actual amount of wait time for servicework. For instance, each actual amount of wait time for service work maybe subtracted from each respective predicted amount of wait time forservice work. In another example, each predicted amount of time forservice work may be compared with each actual amount of time for servicework. For instance, each actual amount of time for service work may besubtracted from each respective predicted amount of time for servicework.

At step 635, a score may be determined from comparisons of predictedtimes may be compared with actual times. In particular embodiments, thedetermined score may be a numerical value. For example, each comparisonmay be added to determine the score. In one instance, a negative scoremay indicate that the service center took less actual amounts of timethan what the service center had predicted. In a second instance, a zeroscore may indicate that predicted amounts of time for the service centermay be typically accurate. In another instance, a positive score mayindicate that the service center took more actual amounts of time thanwhat the service center had predicted. At step 640, the score may beoutputted. In one example, outputting the score may include displayingthe score. For instance, the score may be displayed via a userinterface. In another example, outputting the score may include storingthe score. In one instance, the score may be stored in a memory medium.In another instance, the score may be stored in a data store (e.g.,service center data store 223).

Particular embodiments may repeat one or more steps of the method ofFIG. 6, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 6 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 6 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method for matchingautonomous vehicles with service entities including the particular stepsof the method of FIG. 6, this disclosure contemplates any suitablemethod for matching autonomous vehicles with service entities includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 6, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 6, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 6.

Turning now to FIG. 7, an example method 700 for operating atransportation management system is illustrated, according to one ormore embodiments. At step 710, respective service center candidacyinformation may be received from a multiple service centers. Forexample, transportation management system 110 may receive service centercandidacy information from respective client computer devices 224 ofmultiple service centers. In particular embodiments, service centercandidacy information of a service center may include one or morecapabilities of the service center, a capacity of the service center,one or more skill sets of the service center, location information ofthe service center (e.g., an address of the service center, latitude andlongitude coordinates, etc.), availability information (e.g., times ofday, days of the week, etc.), prices for respective service work, anindication of mobility (e.g., mobile or stationary), types of cleaningservices, estimated times for performing particular services, a numberof charging stations, and/or types of charging stations (e.g., fastcharging stations, regular charging stations, etc.), among others. Inparticular embodiments, UI 242 may be utilized in collecting and/orgathering one or more portions of the service center candidacyinformation.

At step 715, respective availability information associated with theservice centers may be stored in a data store. For example,transportation management system 110 may store availability informationassociated with the service centers in service center data store 223.For instance, the availability information associated with the servicecenters may indicate one or more times types of services available, oneor more times of day that one or more services are available, one ormore current availabilities to perform service work, and/or one or morefuture availabilities to perform service work, among others.

At step 720, respective capacity information associated with the servicecenters may be stored in a data store. For example, transportationmanagement system 110 may store capacity information associated with theservice centers in service center data store 223. For instance, theinformation associated with the service centers may indicate one or morenumbers of charging stations, one or more numbers of service bays, oneor more numbers of mechanics on staff, one or more numbers of carwashes, and/or one or more numbers of vacuuming stations, among others.

At step 725, autonomous vehicle information from an autonomous vehiclecomputing device associated with an autonomous vehicle may be received.For example, autonomous vehicle computing device 150 associated withautonomous vehicle 160 may provide the autonomous vehicle information.In particular embodiments, the autonomous vehicle information mayinclude location information of autonomous vehicle 160, sensor data ofautonomous vehicle 160, autonomous vehicle configuration data, sensorconfiguration data, and/or information associated with needed servicework, among others. For example, the autonomous vehicle information mayinclude one or more data types. For instance, different data types maybe associated with respective different types of autonomous vehiclesand/or different types of sensors, among others. At 730, it may bedetermined, based at least on the autonomous vehicle information, thatthe autonomous vehicle requires service work. For example, theautonomous vehicle information may indicate one or more of a sensormalfunction, a sensor failure, low tire pressure, oil pressure drop, lowbattery charge, and a cleaning issue, among others.

At 735, a pool of one or more service centers that may provide servicework for the autonomous vehicle may be determined from the multipleservice centers. In one example, determining the pool of service centersthat may provide service work for the autonomous vehicle may includedetermining possible matches of the multiple service centers with theautonomous vehicle to determine the pool of service centers that mayprovide service work for the autonomous vehicle. For instance, the poolof one or more service centers may include at least one third-partyservice center. In a second example, determining the pool of servicecenters that may provide service work for the may include determiningdistances from each of the multiple service centers and the autonomousvehicle. For instance, each of the pool of service centers may be withina threshold distance from the autonomous vehicle. In another example,determining the pool of service centers that may provide service workfor the autonomous vehicle may include determining respective scores forthe multiple service centers. For instance, each score of a respectiveone of the pool of service centers may meet or exceed a threshold value.

In particular embodiments, the pool of service centers that may provideservice work for the autonomous vehicle may include one or morethird-party service centers and/or one or more service centers that areowned/operated by an owner/operator of a fleet of autonomous vehicles.For example, the owner/operator of the fleet of autonomous vehicles mayutilize one or more third-party service centers from the pool of servicecenters. For instance, utilizing a third-party service center may reduceutilization of one or more resources (e.g., energy resources, timeresources, cost resources, etc.) in servicing one or more autonomousvehicles of the fleets of autonomous vehicles. In particularembodiments, matching the autonomous vehicle with a service center maybe based at least on information stored via one or more of data stores218-223.

In particular embodiments, matching the autonomous vehicle with aservice center may be based at least on one or more of a wait time forservice work, a rate for the service work, a capacity for the servicework, a number of service work types offered, an availability time forthe service work, operator cost (e.g., labor, parts, storage, realestate, etc.), utilization of a third-party service center,transportation disruption risks, traffic patterns, road conditions,environmental factors (e.g., temperature, flooding, rain, snow, sleet,ice, etc.), a range of the autonomous vehicle, a maximum speed of theautonomous vehicle, a weather tolerance of the autonomous vehicle, aday/night functionality of the autonomous vehicle, current condition ofthe autonomous vehicle, a service record of a service center, ascheduled maintenance of the autonomous vehicle, a service urgency valuefor the service work (e.g., a location of the service center, a locationof where a service center may be in the future, etc.), an inventory ofone or more items for the service work, a location for the service work,predicted amounts of wait time for service work, predicted amounts oftime for service work, and computed scores for respective ones of theservice centers, among others. For example, determining the pool ofservice centers may include determining scores for the multiple servicecenters to determine the pool of service centers. For instance, themethod described with reference to FIG. 3 may be utilized in determiningscores for the multiple service centers with the autonomous vehicle todetermine the pool of that may provide service work for the autonomousvehicle. In particular embodiments, a threshold value may be utilized indetermine the pool of that may provide service work for the autonomousvehicle. In one example, service centers with a score above thethreshold value may be included in the pool of service centers that mayprovide service work. In another example, service centers with a scorebelow the threshold value may be included in the pool of service centersthat may provide service work.

At 740, one or more request messages may be provided to respective oneor more client computer devices 224 of the pool of service centers. Forexample, the one or more messages may include information indicatingthat the autonomous vehicle needs service work. For instance, the one ormore messages may also include information indicating a compensationamount for the service work, location information associated with theautonomous vehicle, and/or a status of the autonomous vehicle (e.g.,operable to drive to a service center, needs a human driver, needs a towtruck, etc.), among others.

At 745, one or more offer messages that offer to provide the servicework for the autonomous vehicle from one or more client computer devices224 of the pool of service centers may be received. At 750, a servicecenter of the pool of service centers may be selected. In one example, aservice center of the pool of service centers may be selected, based atleast on the one or more messages that offer to provide the service workfor the autonomous vehicle. In another example, a service center of thepool of service centers may be selected, based at least on a reductionin a utilization of at least one resource associated with the servicework that will be expended.

In one instance, selecting the service center may reduce utilization ofan energy resource (e.g., battery energy expended, petrol fuel energyexpended, etc.) associated with an autonomous vehicle 160, as theselected service center may be closer to the autonomous vehicle thananother service center and/or the selected service center may at a loweraltitude than the other service center. In a second instance, selectingthe service center may reduce utilization of a first time resourceassociated with a first amount of time to perform the service work(e.g., at least a portion of an amount of time that autonomous vehicle160 may not be available to transport one or more users 135), as theselected service center may perform the service work faster than anotherservice center. In a third instance, selecting the service center mayreduce utilization of a second time resource associated with a secondamount of travel time associated with the service work (e.g., at least aportion of an amount of time that autonomous vehicle 160 may not beavailable to transport one or more users 135), as a travel time to theselected service center may be less than a travel time to anotherservice center. In a fourth instance, selecting the service center mayreduce utilization of a third time resource associated with a thirdamount of wait time associated with waiting for service work to beperformed (e.g., at least a portion of an amount of time that autonomousvehicle 160 may not be available to transport one or more users 135), asa wait time for service work to be performed at the selected servicecenter may be less than a wait time for service work to be performed atanother service center. In another instance, selecting the servicecenter may reduce utilization of a cost resource (e.g., an amount ofcompensation for service work associated with autonomous vehicle 160).

In particular embodiments, resource utilization attributes may beassociated with each service center of the pool of service centers. Forexample, the resource utilization attributes associated with a servicecenter of the pool of service centers may include one or more of: anenergy resource utilization attribute (e.g., battery energy expended,petrol fuel energy expended, etc.) associated with an autonomous vehicle160, as autonomous vehicle 160 may be at a distance from the servicecenter and/or autonomous vehicle 160 may be at lower or higher altitudethan the service center; a first time resource utilization attributeassociated with a first amount of time to perform the service work(e.g., at least a portion of an amount of time that autonomous vehicle160 may not be available to transport one or more users 135); a secondtime resource utilization attribute associated with a second amount oftravel time associated with the service work (e.g., at least a portionof an amount of time that autonomous vehicle 160 may not be available totransport one or more users 135); a third time resource utilizationattribute associated with a third amount of wait time associated withwaiting for service work to be performed (e.g., at least a portion of anamount of time that autonomous vehicle 160 may not be available totransport one or more users 135); and a cost resource utilizationattribute (e.g., an amount of compensation for service work associatedwith autonomous vehicle 160), among others. For instance, numericalvalues may be respectively associated with the utilization attributes.

In particular embodiments, an optimization process may be performedutilizing the numerical values associated with the utilizationattributes of each of the pool of service centers. For example, theoptimization process may include one or more of a linear optimization(e.g., linear programming, etc.) and a non-linear optimization (e.g.,non-linear programming, etc.), among others. For instance, theoptimization process may process attributes of each of the pool ofservice centers to produce one or more minimum numbers or one or moremaximum numbers associated with a service center of the pool of servicecenters. In particular embodiments, the service center associated withthe one or more minimum numbers or the one or more maximum numbers maybe selected to service autonomous vehicle 160. For example, theoptimization process may determine a reduction of at least one resource,that will be expended, associated with the service work for autonomousvehicle 160. For instance, the one or more minimum numbers or the one ormore maximum numbers may be based on one or more minimum numbers or oneor more maximum numbers associated with the numerical values associatedwith the resource utilization attributes associated with the selectedservice center.

In one or more embodiments, selecting, based at least on a reduction ina utilization of at least one resource associated with the service workthat will be expended, a service center from the pool of service centersmay include utilizing the optimization process that may determine areduction of at least one resource, that will be expended, associatedwith the service work for autonomous vehicle 160. For example, theoptimization process may determine a reduction of at least one resource,that will be expended, associated with the service work for autonomousvehicle 160 and may produce one or more minimum numbers or one or moremaximum numbers associated with a service center of the pool of servicecenters, which may indicate the service center should be selected. Forinstance, selecting, based at least on a reduction in a utilization ofat least one resource associated with the service work that will beexpended, a service center from the pool of service centers may includeselecting the service center indicated by the optimization process viathe one or more minimum numbers or the one or more maximum numbers.

In particular embodiments, the selected service center may be or includea third-party service center. In one example, selecting the third-partyservice center may reduce utilization of an energy resource, as thethird-party service center may closer to the autonomous vehicle than aservice center that is owned/operated by the owner/operator of the fleetof autonomous vehicles. In a second example, selecting the third-partyservice center may reduce utilization of a time resource, as a traveltime to the third-party service center may be less than a travel time toa service center that is owned/operated by the owner/operator of thefleet of autonomous vehicles or as the third-party service center may beable to provide the service work in less time than the service centerthat is owned/operated by the owner/operator of the fleet of autonomousvehicles. In another example, selecting the third-party service centermay reduce utilization of a cost resource, as the third-party servicecenter may be able to provide the service work with less cost than theservice center that is owned/operated by the owner/operator of the fleetof autonomous vehicles.

In particular embodiments, selecting the service center from the pool ofservice centers may include selecting the service center from the poolof service centers based at least on determining that a score associatedwith the selected service center is superior to each score associatedwith other service centers of the pool of service centers. In oneexample, the score associated with the selected service center may besuperior to each score associated with other service centers of the poolof service centers such that the score associated with the selectedservice center may be greater than each score associated with otherservice centers of the pool of service centers. In another example, thescore associated with the selected service center may be superior toeach score associated with other service centers of the pool of servicecenters such that the score associated with the selected service centermay be less than each score associated with other service centers of thepool of service centers.

In particular embodiments, periodic updates may be received from servicecenters. For example, the periodic updates may include availability toprovide service work for one or more autonomous vehicles. For instance,the periodic updates may include one or more requests for types ofservice that may be performed. In particular embodiments, an offer for aservice center to perform service work may be provided to the pool ofservice centers until a service center accepts the offer. For example,the offer may be provided to the pool of service centers in a sequentialfashion over a period of time.

At 755, location information of the selected service center may beprovided to the autonomous vehicle computing device. For example, thelocation information may include an address and/or coordinates. Forinstance, the coordinates may include latitude and longitudecoordinates. In particular embodiments, it may be determined that theautonomous vehicle should not travel. For example, the locationinformation may indicate a current location of the autonomous vehicle.For instance, a mobile service center, a human driver, or a tow truckmay be dispatched to the current location of the autonomous vehicle.

At 760, service information associated with the autonomous vehicle maybe provided to a computing device associated with the selected servicecenter. For example, the service center information may be provided to aclient computer devices 224 of the selected service center. Inparticular embodiments, the service center information may includelocation information associated with the autonomous vehicle, anestimated time of arrival of the autonomous vehicle, a list of servicework associated with the autonomous vehicle, and/or access informationand/or authorization information that may be utilized by the servicecenter to access the autonomous vehicle, among others.

Particular embodiments may repeat one or more steps of the method ofFIG. 7, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 7 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 7 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method for matchingautonomous vehicles with service entities including the particular stepsof the method of FIG. 7, this disclosure contemplates any suitablemethod for matching autonomous vehicles with service entities includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 7, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 7, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 7.

Turning now to FIG. 8, another example block diagram of transportationmanagement environment 100 is illustrated, according to one or moreembodiments. In particular embodiments, environment 100 may includevarious computing entities, such as user computing device 130 of user135 (e.g., a ride provider or requestor), transportation managementsystem 110, autonomous vehicle 160, and one or more third-party systems870. The computing entities may be communicatively connected over anysuitable network 120. As an example and not by way of limitation, one ormore portions of network 120 may include an ad hoc network, an extranet,a VPN, a LAN, a WLAN, a WAN, a wireless WAN (WWAN), a MAN, a portion ofthe Internet, a portion of a PSTN, a cellular network, or a combinationof any of the above. In particular embodiments, any suitable networkarrangement and protocol enabling the computing entities to communicatewith each other may be used. Although FIG. 8 illustrates a single userdevice 130, a single transportation management system 110, a singleautonomous vehicle 160, a single third-party system 870, and a singlenetwork 120, this disclosure contemplates any suitable number of each ofthese entities. As an example and not by way of limitation, environment100 may include multiple users 135, user devices 130, multipletransportation management systems 110, multiple autonomous vehicles 160,multiple third-party systems 870, and/or multiple networks 120, amongothers.

User device 130, transportation management system 110, autonomousvehicle 160, and third-party system 870 may be communicatively connectedor co-located with each other in whole or in part. These computingentities may communicate via different transmission technologies andnetwork types. For example, user device 130 and autonomous vehicle 160may communicate with each other via a cable or short-range wirelesscommunication (e.g., Bluetooth, NFC, WI-FI, etc.), and together they maybe connected to the Internet via a cellular network accessible to eitherone of the devices (e.g., user device 130 may be a smartphone with LTEconnection). Transportation management system 110 and third-party system870, on the other hand, may be connected to the Internet via theirrespective LAN/WLAN networks and Internet Service Providers (ISP). FIG.8 illustrates transmission links 850 that connect user device 130,autonomous vehicle 160, transportation management system 110, andthird-party system 870 to communication network 120. This disclosurecontemplates any suitable transmission links 850, including, e.g., wireconnections (e.g., USB, Lightning, Digital Subscriber Line (DSL) or DataOver Cable Service Interface Specification (DOCSIS)), wirelessconnections (e.g., WI-FI, WiMAX, cellular, satellite, NFC, Bluetooth),optical connections (e.g., Synchronous Optical Networking (SONET),Synchronous Digital Hierarchy (SDH)), any other wireless communicationtechnologies, and any combination thereof. In particular embodiments,one or more links 850 may connect to one or more networks 120, which mayinclude in part, e.g., ad hoc network, the Intranet, extranet, VPN, LAN,WLAN, WAN, WWAN, MAN, PSTN, a cellular network, a satellite network, orany combination thereof. The computing entities need not necessarily usethe same type of transmission link 850. For example, user device 130 maycommunicate with transportation management system 110 via a cellularnetwork and the Internet, but communicate with autonomous vehicle 160via Bluetooth or a physical wire connection.

In particular embodiments, transportation management system 110 mayfulfill ride requests for one or more users 135 by dispatching suitablevehicles. Transportation management system 110 may receive any number ofride requests from any number of ride requestors 135. In particularembodiments, a ride request from a ride requestor 135 may include anidentifier that identifies him/her in transportation management system110. Transportation management system 110 may use the identifier toaccess and store the ride requestor's 135 information, in accordancewith his/her privacy settings. Ride requestor's 135 information may bestored in one or more data stores (e.g., a relational database system)associated with and accessible to transportation management system 110.In particular embodiments, ride requestor information may includeprofile information about a particular ride requestor 135. In particularembodiments, ride requestor 135 may be associated with one or morecategories or types, through which the ride requestor 135 may beassociated with aggregate information about certain ride requestors ofthose categories or types. Ride information may include, for example,preferred pick-up and drop-off locations, driving preferences (e.g.,safety comfort level, preferred speed, rates ofacceleration/deceleration, safety distance from other vehicles whentravelling at various speeds, route, etc.), entertainment preferencesand settings (e.g., preferred music genre or playlist, audio volume,display brightness, etc.), temperature settings, whether conversationwith the driver is welcomed, frequent destinations, historical ridingpatterns (e.g., time of day of travel, starting and ending locations,etc.), preferred language, age, gender, or any other suitableinformation. In particular embodiments, transportation management system110 may classify a user 135 based on known information about the user135 (e.g., using machine-learning classifiers), and use theclassification to retrieve relevant aggregate information associatedwith that class. For example, the system 110 may classify a user 135 asa teenager and retrieve relevant aggregate information associated withteenagers, such as the type of music generally preferred by teenagers.

Transportation management system 110 may also store and access rideinformation. Ride information may include locations related to the ride,traffic data, route options, optimal pick-up or drop-off locations forthe ride, or any other suitable information associated with a ride. Asan example and not by way of limitation, when transportation managementsystem 110 receives a request to travel from San Francisco InternationalAirport (SFO) to Palo Alto, Calif., transportation management system 110may access or generate any relevant ride information for this particularride request. The ride information may include, for example, preferredpick-up locations at SFO; alternate pick-up locations in the event thata pick-up location is incompatible with the ride requestor (e.g., theride requestor may be disabled and cannot access the pick-up location)or the pick-up location is otherwise unavailable due to construction,traffic congestion, changes in pick-up/drop-off rules, or any otherreason; one or more routes to navigate from SFO to Palo Alto; preferredoff-ramps for a type of user; or any other suitable informationassociated with the ride. In particular embodiments, portions of theride information may be based on historical data associated withhistorical rides facilitated by transportation management system 110.For example, historical data may include aggregate information generatedbased on past ride information, which may include any ride informationdescribed herein and telemetry data collected by sensors in autonomousvehicles and/or user devices. Historical data may be associated with aparticular user (e.g., that particular user's preferences, commonroutes, etc.), a category/class of users (e.g., based on demographics),and/or all users of transportation management system 110. For example,historical data specific to a single user may include information aboutpast rides that particular user has taken, including the locations atwhich the user is picked up and dropped off, music the user likes tolisten to, traffic information associated with the rides, time of theday the user most often rides, and any other suitable informationspecific to the user. As another example, historical data associatedwith a category/class of users may include, e.g., common or popular ridepreferences of users in that category/class, such as teenagerspreferring pop music, ride requestors who frequently commute to thefinancial district may prefer to listen to news, etc. As yet anotherexample, historical data associated with all users may include generalusage trends, such as traffic and ride patterns. Using historical data,transportation management system 110 in particular embodiments maypredict and provide ride suggestions in response to a ride request. Inparticular embodiments, transportation management system 110 may usemachine-learning, such as neural-networks, regression processes,instance-based processes (e.g., k-Nearest Neighbor), decision-treeprocesses, Bayesian processes, clustering processes,association-rule-learning processes, deep-learning processes,dimensionality-reduction processes, ensemble processes, and any othersuitable machine-learning processes known to persons of ordinary skillin the art. The machine-learning models may be trained using anysuitable training process, including supervised learning based onlabeled training data, unsupervised learning based on unlabeled trainingdata, and/or semi-supervised learning based on a mixture of labeled andunlabeled training data.

In particular embodiments, transportation management system 110 mayinclude one or more servers. Each server may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.The servers may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server may include hardware, software, orembedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by the server. In particular embodiments, transportationmanagement system 110 may include one or more data stores. The datastores may be used to store various types of information, such as rideinformation, ride requestor information, ride provider information,historical information, third-party information, or any other suitabletype of information. In particular embodiments, the information storedin the data stores may be organized according to specific datastructures. In particular embodiments, each data store may be arelational, columnar, correlation, or other suitable database system.Although this disclosure describes or illustrates particular types ofdatabases, this disclosure contemplates any suitable types of databases.Particular embodiments may provide interfaces that enable a user device130 (which may belong to a ride requestor or provider), a transportationmanagement system 110, vehicle system 160, or a third-party system 870to process, transform, manage, retrieve, modify, add, or delete theinformation stored in data store.

In particular embodiments, transportation management system 110 mayinclude an authorization server (or other suitable component(s)) thatallows users 135 to opt-in to or opt-out of having their information andactions logged, recorded, or sensed by transportation management system110 or shared with other systems (e.g., third-party systems 870). Inparticular embodiments, a user 135 may opt-in or opt-out by settingappropriate privacy settings. A privacy setting of a user may determinewhat information associated with the user may be logged, how informationassociated with the user may be logged, when information associated withthe user may be logged, who may log information associated with theuser, whom information associated with the user may be shared with, andfor what purposes information associated with the user may be logged orshared. Authorization servers may be used to enforce one or more privacysettings of the users 135 of transportation management system 110through blocking, data hashing, anonymization, or other suitabletechniques as appropriate.

In particular embodiments, third-party system 870 may be anetwork-addressable computing system that may host GPS maps, customerreviews, music or content, weather information, or any other suitabletype of information. Third-party system 870 may generate, store,receive, and send relevant data, such as, for example, map data,customer review data from a customer review website, weather data, orany other suitable type of data. Third-party system 870 may be accessedby the other computing entities of the network environment eitherdirectly or via network 120. For example, user device 130 may accessthird-party system 870 via network 120, or via transportation managementsystem 110. In the latter case, if credentials are required to accessthird-party system 870, user 135 may provide such information to thetransportation management system 110, which may serve as a proxy foraccessing content from third-party system 870.

In particular embodiments, user device 130 may be a mobile computingdevice such as a smartphone, tablet computer, or laptop computer. Userdevice 130 may include one or more processors (e.g., CPU and/or GPU),memory, and storage. An operation system and applications may beinstalled on user device 130, such as, e.g., a transportationapplication associated with the transportation management system 110,applications associated with third-party systems 870, and applicationsassociated with the operating system. User device 130 may includefunctionality for determining its location, direction, or orientation,based on integrated sensors such as GPS, compass, gyroscope, oraccelerometer. User device 130 may also include wireless transceiversfor wireless communication, and may support wireless communicationprotocols such as Bluetooth, near-field communication (NFC), infrared(IR) communication, WI-FI, and/or 2G/3G/4G/LTE mobile communicationstandard. User device 130 may also include one or more cameras,scanners, touchscreens, microphones, speakers, and any other suitableinput-output devices.

In particular embodiments, autonomous vehicle 160 may be an autonomousvehicle and equipped with an array of sensors 844, a navigation system846, and a ride-service computing device 848. In particular embodiments,a fleet of autonomous vehicles 160 may be managed by transportationmanagement system 110. The fleet of autonomous vehicles 160, in whole orin part, may be owned by the entity associated with transportationmanagement system 110, or they may be owned by a third-party entityrelative to transportation management system 110. In either case,transportation management system 110 may control the operations ofautonomous vehicles 160, including, e.g., dispatching select autonomousvehicle 160 to fulfill ride requests, instructing autonomous vehicles160 to perform select operations (e.g., head to a service center orcharging/fueling station, pull over, stop immediately, self-diagnose,lock/unlock compartments, change music station, change temperature, andany other suitable operations), and instructing autonomous vehicles 160to enter select operation modes (e.g., operate normally, drive at areduced speed, drive under the command of human operators, and any othersuitable operational modes).

In particular embodiments, autonomous vehicles 160 may receive data fromand transmit data to transportation management system 110 andthird-party system 870. Example of received data may include, e.g.,instructions, new software or software updates, maps, three-dimensional(3D) models, trained or untrained machine-learning models, locationinformation (e.g., location of the ride requestor, autonomous vehicle160 itself, other autonomous vehicles 160, and target destinations suchas service centers), navigation information, traffic information,weather information, entertainment content (e.g., music, video, andnews) ride requestor information, ride information, and any othersuitable information. Examples of data transmitted from the autonomousvehicle 160 may include, e.g., telemetry and sensor data,determinations/decisions based on such data, vehicle condition or state(e.g., battery/fuel level, tire and brake conditions, sensor condition,speed, odometer, etc.), location, navigation data, passenger inputs(e.g., through a user interface in autonomous vehicle 160, passengersmay send/receive data to transportation management system 110 and/orthird-party system 870), and any other suitable data.

In particular embodiments, autonomous vehicles 160 may also communicatewith each other as well as other traditional human-driven vehicles,including those managed and not managed by the transportation managementsystem 110. For example, one autonomous vehicle 160 may communicate withanother autonomous vehicle 160 data regarding their respective location,condition, status, sensor reading, and any other suitable information.In particular embodiments, vehicle-to-vehicle communication may takeplace over direct short-range wireless connection (e.g., WI-FI,Bluetooth, NFC) and/or over a network (e.g., the Internet or viatransportation management system 110 or third-party system 870).

In particular embodiments, an autonomous vehicle 160 may obtain andprocess sensor/telemetry data. Such data may be captured by any suitablesensors. For example, autonomous vehicle 160 may have a Light Detectionand Ranging (LiDAR) sensor array of multiple LiDAR transceivers that areconfigured to rotate 360°, emitting pulsed laser light and measuring thereflected light from objects surrounding vehicle 160. In particularembodiments, LiDAR transmitting signals may be steered by use of a gatedlight valve, which may be a MEMs device that directs a light beam usingthe principle of light diffraction. Such a device may not use a gimbaledmirror to steer light beams in 360° around the autonomous vehicle.Rather, the gated light valve may direct the light beam into one ofseveral optical fibers, which may be arranged such that the light beammay be directed to many discrete positions around the autonomousvehicle. Thus, data may be captured in 360° around the autonomousvehicle, but no rotating parts may be necessary. A LiDAR is an effectivesensor for measuring distances to targets, and as such may be used togenerate a 3D model of the external environment of autonomous vehicle160. As an example and not by way of limitation, the 3D model mayrepresent the external environment including objects such as other cars,curbs, debris, objects, and pedestrians up to a maximum range of thesensor arrangement (e.g., 50, 100, or 200 meters). As another example,the autonomous vehicle 160 may have optical cameras pointing indifferent directions. The cameras may be used for, e.g., recognizingroads, lane markings, street signs, traffic lights, police, othervehicles, and any other visible objects of interest. To enable vehicle160 to “see” at night, infrared cameras may be installed. In particularembodiments, the vehicle may be equipped with stereo vision for, e.g.,spotting hazards such as pedestrians or tree branches on the road. Asanother example, autonomous vehicle 160 may have radars for, e.g.,detecting other vehicles and/or hazards afar. Furthermore, the vehicle160 may have ultra sound equipment for, e.g., parking and obstacledetection. In addition to sensors enabling autonomous vehicle 160 todetect, measure, and understand the external world around it, autonomousvehicle 160 may further be equipped with sensors for detecting andself-diagnosing its own state and condition. For example, autonomousvehicle 160 may have wheel sensors for, e.g., measuring velocity; globalpositioning system (GPS) for, e.g., determining the vehicle's currentgeolocation; and/or inertial measurement units, accelerometers,gyroscopes, and/or odometer systems for movement or motion detection.While the description of these sensors provides particular examples ofutility, one of ordinary skill in the art would appreciate that theutilities of the sensors are not limited to those examples. Further,while an example of a utility may be described with respect to aparticular type of sensor, it should be appreciated that the utility maybe achieving using any combination of sensors. For example, anautonomous vehicle 160 may build a 3D model of its surrounding based ondata from its LiDAR, radar, sonar, and cameras, along with apre-generated map obtained from transportation management system 110 orthird-party system 870. Although sensors 844 appear in a particularlocation on autonomous vehicle 160 in FIG. 8, sensors 844 may be locatedin any suitable location in and/or on autonomous vehicle 160. Examplelocations for sensors include the front and rear bumpers, the doors, thefront windshield, on the side paneling, or any other suitable location.

In particular embodiments, autonomous vehicle 160 may be equipped with aprocessing unit (e.g., one or more CPUs and GPUs), memory, and storage.Autonomous vehicle 160 may thus be equipped to perform a variety ofcomputational and processing tasks, including processing the sensordata, extracting useful information, and operating accordingly. Forexample, based on images captured by its cameras and a machine-visionmodel, autonomous vehicle 160 may identify particular types of objectscaptured by the images, such as pedestrians, other vehicles, lanes,curbs, and any other objects of interest.

In particular embodiments, autonomous vehicle 160 may have a navigationsystem 846 responsible for safely navigating autonomous vehicle 160. Inparticular embodiments, navigation system 846 may take as input any typeof sensor data from, e.g., a Global Positioning System (GPS) module,inertial measurement unit (IMU), LiDAR sensors, optical cameras, radiofrequency (RF) transceivers, or any other suitable telemetry or sensorymechanisms. Navigation system 846 may also utilize, e.g., map data,traffic data, accident reports, weather reports, instructions, targetdestinations, and any other suitable information to determine navigationroutes and particular driving operations (e.g., slowing down, speedingup, stopping, swerving, etc.). In particular embodiments, the navigationsystem 846 may use its determinations to control autonomous vehicle 160to operate in prescribed manners and to guide autonomous vehicle 160 toits destinations without colliding into other objects. Although thephysical embodiment of the navigation system 846 (e.g., the processingunit) appears in a particular location on autonomous vehicle 160 in FIG.8, navigation system 846 may be located in any suitable location in oron autonomous vehicle 160. Example locations for navigation system 846include inside the cabin or passenger compartment of autonomous vehicle160, near the engine/battery, near the front seats, rear seats, or inany other suitable location.

In particular embodiments, autonomous vehicle 160 may be equipped with aride-service computing device 848, which may be a tablet or othersuitable device installed by transportation management system 110 toallow the user to interact with the autonomous vehicle 160,transportation management system 110, other users 135, or third-partysystems 870. In particular embodiments, installation of ride-servicecomputing device 848 may be accomplished by placing ride-servicecomputing device 848 inside autonomous vehicle 160, and configuring itto communicate with the vehicle 160 via a wire or wireless connection(e.g., via Bluetooth). Although FIG. 8 illustrates a single ride-servicecomputing device 848 at a particular location in autonomous vehicle 160,autonomous vehicle 160 may include several ride-service computingdevices 848 in several different locations within autonomous vehicle160. As an example and not by way of limitation, autonomous vehicle 160may include four ride-service computing devices 848 located in thefollowing places: one in front of the front-left passenger seat (e.g.,driver's seat in traditional U.S. automobiles), one in front of thefront-right passenger seat, one in front of each of the rear-left andrear-right passenger seats. In particular embodiments, ride-servicecomputing device 848 may be detachable from any component of autonomousvehicle 160. This may allow users to handle ride-service computingdevice 848 in a manner consistent with other tablet computing devices.As an example and not by way of limitation, a user may move ride-servicecomputing device 848 to any location in the cabin or passengercompartment of autonomous vehicle 160, may hold ride-service computingdevice 848 in his/her lap, or handle ride-service computing device 848in any other suitable manner. In particular embodiments, ride-servicecomputing device 848 may include one or more structures and/or one ormore functionalities as those described with reference to autonomousvehicle computing device 150. Although this disclosure describesproviding a particular computing device in a particular manner, thisdisclosure contemplates providing any suitable computing device in anysuitable manner.

Turning now to FIG. 9, an example computer system 900 is illustrated,according to one or more embodiments. In particular embodiments, one ormore computer systems 900 perform one or more steps of one or moremethods described or illustrated herein. In particular embodiments, oneor more computer systems 900 provide functionality described orillustrated herein. In particular embodiments, software running on oneor more computer systems 900 performs one or more steps of one or moremethods described or illustrated herein or provides functionalitydescribed or illustrated herein. Particular embodiments include one ormore portions of one or more computer systems 900. Herein, reference toa computer system may encompass a computing device, and vice versa,where appropriate. Moreover, reference to a computer system mayencompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems900. This disclosure contemplates computer system 900 taking anysuitable physical form. As example and not by way of limitation,computer system 900 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, anaugmented/virtual reality device, or a combination of two or more ofthese. Where appropriate, computer system 900 may include one or morecomputer systems 900; be unitary or distributed; span multiplelocations; span multiple machines; span multiple data centers; or residein a cloud, which may include one or more cloud components in one ormore networks. Where appropriate, one or more computer systems 900 mayperform without substantial spatial or temporal limitation one or moresteps of one or more methods described or illustrated herein. As anexample and not by way of limitation, one or more computer systems 900may perform in real time or in batch mode one or more steps of one ormore methods described or illustrated herein. One or more computersystems 900 may perform at different times or at different locations oneor more steps of one or more methods described or illustrated herein,where appropriate.

In particular embodiments, computer system 900 includes a processor 902,memory 904, storage 906, an input/output (I/O) interface 908, acommunication interface 910, and a bus 912. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 902 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 902 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 904, or storage 906; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 904, or storage 906. In particular embodiments, processor902 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 902 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 902 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 904 or storage 906, andthe instruction caches may speed up retrieval of those instructions byprocessor 902. Data in the data caches may be copies of data in memory904 or storage 906 for instructions executing at processor 902 tooperate on; the results of previous instructions executed at processor902 for access by subsequent instructions executing at processor 902 orfor writing to memory 904 or storage 906; or other suitable data. Thedata caches may speed up read or write operations by processor 902. TheTLBs may speed up virtual-address translation for processor 902. Inparticular embodiments, processor 902 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 902 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 902may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 902. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 904 includes main memory for storinginstructions for processor 902 to execute or data for processor 902 tooperate on. As an example and not by way of limitation, computer system900 may load instructions from storage 906 or another source (such as,for example, another computer system 900) to memory 904. Processor 902may then load the instructions from memory 904 to an internal registeror internal cache. To execute the instructions, processor 902 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 902 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor902 may then write one or more of those results to memory 904. Inparticular embodiments, processor 902 executes only instructions in oneor more internal registers or internal caches or in memory 904 (asopposed to storage 906 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 904 (as opposedto storage 906 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 902 tomemory 904. Bus 912 may include one or more memory buses, as describedin further detail below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 902 and memory 904 andfacilitate accesses to memory 904 requested by processor 902. Inparticular embodiments, memory 904 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 904 may include one ormore memories 904, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 906 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 906may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage906 may include removable or non-removable (or fixed) media, whereappropriate. Storage 906 may be internal or external to computer system900, where appropriate. In particular embodiments, storage 906 isnon-volatile, solid-state memory. In particular embodiments, storage 906includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 906 taking any suitable physicalform. Storage 906 may include one or more storage control unitsfacilitating communication between processor 902 and storage 906, whereappropriate. Where appropriate, storage 906 may include one or morestorages 906. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 908 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 900 and one or more I/O devices. Computer system900 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 900. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 908 for them. Where appropriate, I/O interface 908 mayinclude one or more device or software drivers enabling processor 902 todrive one or more of these I/O devices. I/O interface 908 may includeone or more I/O interfaces 908, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 910 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 900 and one or more other computer systems 900 or one ormore networks. As an example and not by way of limitation, communicationinterface 910 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 910 for it. As an example and not by way of limitation,computer system 900 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 900 may communicate with a wireless PAN (WPAN)(such as, for example, a Bluetooth WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 900 may include any suitable communication interface 910 for anyof these networks, where appropriate. Communication interface 910 mayinclude one or more communication interfaces 910, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 912 includes hardware, software, or bothcoupling components of computer system 900 to each other. As an exampleand not by way of limitation, bus 912 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 912may include one or more buses 912, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

In particular embodiments, processor 902 may execute processorinstructions in implementing one or more systems, one or moreflowcharts, one or more methods, and/or one or more processes describedherein. In one example, memory 904 may store processor instructionsand/or software, executable by processor 902, that may be utilized inimplementing one or more systems, one or more flowcharts, one or moremethods, and/or one or more processes described herein. In a secondexample, storage 906 may store processor instructions and/or software,executable by processor 902, that may be utilized in implementing one ormore systems, one or more flowcharts, one or more methods, and/or one ormore processes described herein. In another, processor 902 may executeinstructions received via communication interface 910 that may beutilized in implementing one or more systems, one or more flowcharts,one or more methods, and/or one or more processes described herein. Inparticular embodiments, one or more FPGAs and/or one or more ASICs maybe configure to implement one or more systems, one or more flowcharts,one or more methods, and/or one or more processes described herein

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A system comprising: one or more processors; andone or more computer-readable non-transitory storage media coupled toone or more of the processors, the one or more computer-readablenon-transitory storage media comprising instructions operable whenexecuted by one or more of the processors to cause the system to performoperations comprising: receiving respective service center candidacyinformation from a plurality of service center computers; receivingautonomous vehicle information from an autonomous vehicle computingdevice associated with an autonomous vehicle; determining, based atleast on the autonomous vehicle information, that the autonomous vehiclerequires service work; determining, based on the candidacy informationfrom the plurality of service center computers and the autonomousvehicle information, a pool of service centers that are capable ofproviding the service work for the autonomous vehicle; selecting, basedat least on a reduction in a utilization of at least one resourceassociated with the service work that will be expended, a service centerfrom the pool of service centers; and providing, to the autonomousvehicle computing device associated with the autonomous vehicle,location information of the selected service center.
 2. The system ofclaim 1, wherein the selecting, based at least on the reduction in theutilization of the at least one resource associated with the servicework that will be expended, the service center from the pool of servicecenters includes utilizing an optimization process that determines thereduction of the at least one resource via processing numerical valuesassociated with resource utilization attributes associated with eachservice center of the pool of service centers.
 3. The system of claim 2,wherein the at least one resource includes at least one of an energyresource associated with the autonomous vehicle, a first time resourceassociated with a first amount of time to perform the service work, asecond time resource associated with a second amount of travel timeassociated with the service work, and a third time resource associatedwith a third amount of wait time associated with waiting for the servicework to be performed.
 4. The system of claim 1, wherein the determiningthe pool of service centers includes determining possible matches of theplurality of service centers with the autonomous vehicle.
 5. The systemof claim 4, wherein the determining the possible matches of theplurality of service centers with the autonomous vehicle is based atleast on one or more of: a wait time for the service work, a rate forthe service work, a capacity for the service work, a number of servicework types offered, an availability time for the service work, anoperator cost, transportation disruption risks, traffic patterns, roadconditions, environmental factors, a range of the autonomous vehicle, amaximum speed of the autonomous vehicle, a weather tolerance of theautonomous vehicle, current condition of the autonomous vehicle, aservice record of the service center, a scheduled maintenance of theautonomous vehicle, a service urgency value for the service work, aninventory of one or more items for the service work, a location for theservice work, predicted amounts of wait time for service work, andpredicted amounts of time for service work.
 6. The system of claim 4,wherein the determining the pool of service centers includes determiningrespective scores for the plurality of service centers and determiningthat the score for each of the pool of service centers meets or exceedsa threshold value; and wherein the determining the respective scores forthe plurality of service centers includes weighting a plurality ofattributes associated with each of the plurality of service centers. 7.The system of claim 6, wherein the determining respective scores for theplurality of service centers includes determining a numerical value foreach plurality of attributes associated with each of the plurality ofservice centers; and wherein the selecting the service center from thepool of service centers includes selecting the service center from thepool of service centers based at least on determining that a scoreassociated with the selected service center is superior to each scoreassociated with other service centers of the pool of service centers. 8.The system of claim 3, wherein the processors are further operable whenexecuting the instructions to perform operations comprising: receivingpredicted amounts of wait time for service work, actual amounts of waittime for service work, predicted amounts of time for service work, andactual amounts of time for service work; and determining respectivescores for the plurality of service centers based at least on thepredicted amounts of wait time for service work, the actual amounts ofwait time for service work, the predicted amounts of time for servicework, and the actual amounts of time for service work; wherein thedetermining the possible matches of the plurality of service centerswith the autonomous vehicle is based at least on the respective scoresof the plurality of service centers.
 9. The system of claim 1, whereinthe determining the pool of service centers includes determining adistance between each of the plurality of service centers to theautonomous vehicle, wherein the distance between each of the pool ofservice centers to the autonomous vehicle is within a thresholddistance.
 10. The system of claim 1, wherein the processors are furtheroperable when executing the instructions to perform operationscomprising: providing, to a computing device associated with theselected service center, service information associated with theautonomous vehicle.
 11. A method comprising, by a computing system:receiving respective service center candidacy information from aplurality of service center computers; receiving autonomous vehicleinformation from an autonomous vehicle computing device associated withan autonomous vehicle; determining, based at least on the autonomousvehicle information, that the autonomous vehicle requires service work;determining, based on the candidacy information from the plurality ofservice center computers and the autonomous vehicle information, a poolof service centers that are capable of providing the service work forthe autonomous vehicle; selecting, based at least on a reduction in autilization of at least one resource, a service center from the pool ofservice centers; and providing, to the autonomous vehicle computingdevice associated with the autonomous vehicle, location information ofthe selected service center.
 12. The method of claim 11, wherein theselecting, based at least on the reduction in the utilization of the atleast one resource associated with the service work that will beexpended, the service center from the pool of service centers includesutilizing an optimization process that determines the reduction of theat least one resource via processing numerical values associated withresource utilization attributes associated with each service center ofthe pool of service centers.
 13. The method of claim 12, wherein the atleast one resource includes at least one of an energy resourceassociated with the autonomous vehicle, a first time resource associatedwith a first amount of time to perform the service work, a second timeresource associated with a second amount of travel time associated withthe service work, and a third time resource associated with a thirdamount of wait time associated with waiting for the service work to beperformed.
 14. The method of claim 11, wherein the determining the poolof service centers includes determining possible matches of theplurality of service centers with the autonomous vehicle.
 15. The methodof claim 14, wherein the determining the possible matches of theplurality of service centers with the autonomous vehicle is based atleast on one or more of: a wait time for the service work, a rate forthe service work, a capacity for the service work, a number of servicework types offered, an availability time for the service work, anoperator cost, transportation disruption risks, traffic patterns, roadconditions, environmental factors, a range of the autonomous vehicle, amaximum speed of the autonomous vehicle, a weather tolerance of theautonomous vehicle, current condition of the autonomous vehicle, aservice record of the service center, a scheduled maintenance of theautonomous vehicle, a service urgency value for the service work, aninventory of one or more items for the service work, a location for theservice work, predicted amounts of wait time for service work, andpredicted amounts of time for service work.
 16. The method of claim 14,further comprising: receiving predicted amounts of wait time for servicework, actual amounts of wait time for service work, predicted amounts oftime for service work, and actual amounts of time for service work; anddetermining respective scores for the plurality of service centers basedat least on the predicted amounts of wait time for service work, theactual amounts of wait time for service work, the predicted amounts oftime for service work, and the actual amounts of time for service work;wherein the determining the possible matches of the plurality of servicecenters with the autonomous vehicle is based at least on the respectivescores of the plurality of service centers.
 17. The method of claim 14,wherein the determining the pool of service centers includes determiningrespective scores for the plurality of service centers and determiningthat the score for each of the pool of service centers meets or exceedsa threshold value; wherein the determining the respective scores for theplurality of service centers includes weighting a plurality ofattributes associated with each of the plurality of service centers;wherein the determining respective scores for the plurality of servicecenters includes determining a numerical value for each plurality ofattributes associated with each of the plurality of service centers; andwherein the selecting the service center from the pool of servicecenters includes selecting the service center from the pool of servicecenters based at least on determining that a score associated with theselected service center is superior to each score associated with otherservice centers of the pool of service centers.
 18. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to cause one or more processors to performoperations comprising: receiving respective service center candidacyinformation from a plurality of service center computers; receivingautonomous vehicle information from an autonomous vehicle computingdevice associated with an autonomous vehicle; determining, based atleast on the autonomous vehicle information, that the autonomous vehiclerequires service work; determining, based on the candidacy informationfrom the plurality of service center computers and the autonomousvehicle information, a pool of service centers that are capable ofproviding the service work for the autonomous vehicle; selecting, basedat least on a reduction in a utilization of at least one resource, aservice center from the pool of service centers; and providing, to theautonomous vehicle computing device associated with the autonomousvehicle, location information of the selected service center.
 19. Themedia of claim 18, wherein the selecting, based at least on thereduction in the utilization of the at least one resource associatedwith the service work that will be expended, the service center from thepool of service centers includes utilizing an optimization process thatdetermines the reduction of the at least one resource via processingnumerical values associated with resource utilization attributesassociated with each service center of the pool of service centers. 20.The media of claim 19, wherein the at least one resource includes atleast one of an energy resource associated with the autonomous vehicle,a first time resource associated with a first amount of time to performthe service work, a second time resource associated with a second amountof travel time associated with the service work, and a third timeresource associated with a third amount of wait time associated withwaiting for the service work to be performed.